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An hphp-Adaptive Sampling Algorithm for Dispersion Relation Reconstruction of 3D Photonic Crystals

Yueqi Wang Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. Email: [email protected] YW acknowledges support from the Research Grants Council (RGC) of Hong Kong via the Hong Kong PhD Fellowship Scheme (HKPFS).    Richard Craster Department of Mathematics, Imperial College London, London SW7 2AZ,UK. Email:[email protected]    Guanglian Li Corresponding author. Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. Email: [email protected] GL acknowledges the support from Newton International Fellowships Alumni following-on funding awarded by The Royal Society, Young Scientists fund (Project number: 12101520) by NSFC and Early Career Scheme (Project number: 27301921), RGC, Hong Kong.
Abstract

In this work we investigate the computation of dispersion relation (i.e., band functions) for three-dimensional photonic crystals, formulated as a parameterized Maxwell eigenvalue problem, using a novel hphp-adaptive sampling algorithm. We develop an adaptive sampling algorithm in the parameter domain such that local elements with singular points are refined at each iteration, construct a conforming element-wise polynomial space on the adaptive mesh such that the distribution of the local polynomial spaces reflects the regularity of the band functions, and define an element-wise Lagrange interpolation operator to approximate the band functions. We rigorously prove the convergence of the algorithm. To illustrate the significant potential of the algorithm, we present two numerical tests with band gap optimization.
Key words: hphp-adaptive sampling algorithm, photonic crystals, band gap maximization, parameterized Maxwell eigenvalue problem

1 Introduction

Photonic crystals (PhCs) are periodic nanostructures typically made from dielectric materials and are widely used in optics for wave manipulation and control [26]. For some specially designed PhCs structures, electromagnetic waves with frequencies in certain ranges cannot propagate in the crystals; these prohibited ranges are so-called band gaps and the knowledge of their position or the ability to design for specific ranges underpin many important applications, including optical transistors, photonic fibers and low-loss optical mirrors [54, 43, 49, 29]. The dispersion relation describes the dependence of wave frequency on its wave vector when the wave passes through certain materials. Photonic band gaps offer numerous benefits in practical applications, e.g., waveguides, cavities, and photonic crystal fibers [12, 8, 52, 32]. Notably there is a parallel field in acoustics and mechanical vibration, phononic crystals [30], where band gaps and periodic media are used to filter and control elastic waves and design devices. In binary systems (i.e., PhCs composed of two materials), the appearance, size, and location of band gaps rely on various factors, including filling fraction, lattice symmetry, topology of constituent material phases, and the contrast between the permittivity of materials [26]. By effectively controlling these parameters, it is possible to design periodic composite materials that possess desired photonic band gaps, thereby achieving excellent performance in practical applications.

The behavior of electromagnetic waves is described mathematically by Maxwell’s equations [25] and the band gaps correspond to the gaps in the spectrum of the Maxwell operator with periodic coefficients. Thus, the task of dispersion relation reconstruction or the numerical simulation of electromagnetic waves inside PhCs can be transformed into the approximation of the Maxwell eigenvalue problem. For infinite crystals with perfect periodicity, Bloch’s theorem reduces the task to a set of parameterized Maxwell eigenvalue problems defined in the unit cell with periodic boundary conditions. The associated parameter is the so-called wave vector 𝐤\mathbf{k}, which varies in the irreducible Brillouin zone (IBZ) [28]. The nnth band function ωn\omega_{n}, which is the square root of the nnth largest eigenvalue up to a constant, is regarded as a function of the wave vector 𝐤\mathbf{k} for all n+n\in\mathbb{N}^{+} and the band gap represents the separation between two adjacent band functions. Thus, computing the nnth band function ωn(𝐤)\omega_{n}(\mathbf{k}) boils down to solving an infinite number of Maxwell eigenvalue problems. To create these band gaps, it is imperative that the permittivity exhibits distinct values in both the inclusion and the background of the unit cell, and that the magnitude difference, referred to as the “contrast”, is often substantial. However, solving the Maxwell eigenvalue problems with high-contrast and piecewise constant coefficients presents notorious numerical challenges due to, e.g., spurious modes [48], and an indefinite large-scale linear system.

To reduce the computational burden, a natural approach is to reduce the number of parameters 𝐤\mathbf{k} by constraining 𝐤\mathbf{k} to a coarse mesh within the IBZ or its boundaries. Nonetheless, recent research has uncovered instances where tiny band gaps, e.g., the so-called avoided crossings, observed within this simplified approximation, vanish upon the inclusion of additional 𝐤\mathbf{k}-points or the enhancement of eigenvalue precision [23, 40, 13]. This drawback greatly affects some application areas of photonic materials in which the requirements are very sensitive to tiny band gaps. For example, it is important to avoid the smallest band gaps in the manufacture of Large-Pitch PhC fiber for a larger output average power [19] while the leakage channel fibers exploit the smallest band gaps [18]. There is a conflict between desiring high accuracy (i.e., sampling at many 𝐤\mathbf{k} over the IBZ), and the computational cost in doing so for three-dimensional (3D) structures. Thus, it is of huge importance to develop algorithms that use as few sampling points for PhCs band function calculations as possible and yet accurately characterize the band functions.

In this work, we extend the hphp-adaptive sampling method [50] (for 2D PhCs, described by parameterized Helmholtz eigenvalue problem) to the 3D cases, by interpolating the band functions elementwise on an adaptive mesh, and demonstrate its efficiency on the band gap maximization problem [10, 53, 22]. First, we adaptively refine the mesh in the parameter domain, cf., Algorithm 1, by element-wise indicator (3.1) and tolerance (3.2), which adaptively refines elements containing singularities, cf. Theorem 3.1. Second, we construct a conforming finite element space (3.5) on the adaptive mesh, and derive element-wise interpolation by identifying a set of basis functions for the local polynomial space. Third, we establish the hphp-adaptive sampling algorithm in Algorithm 2, and rigorously justify its convergence in Theorems 4.5 and 4.6. The resulting method allows approximating band functions accurately using significantly fewer sampling points, thus making the band gap maximization more efficient.

To demonstrate the efficiency of the algorithm, we employ two models of 3D PhCs, which consist of layered cubic structures with silicon blocks and spheres embedded in air. By optimizing the dimensions of these blocks and the radius of the spheres, we identify an optimal unit cell structure that maximizes the band gap. These experiments show the challenges of calculating the band function of 3D PhCs and showcase the process of the adaptive interpolation algorithm. To verify the performance of Algorithm 2, we display its convergence history and compare it with the uniform refinement.

The rest of this paper is organized as follows. In Section 2, we summarize the derivation of band functions for 3D PhCs and discuss the properties of band functions. We also outline the challenges associated with computing the Maxwell eigenvalue problem and describe the 𝐇(curl)\mathbf{H}(\text{curl})-conforming 𝐤\mathbf{k}-modified Nédélec edge finite element method (FEM). In Section 3, we extend the hphp-adaptive sampling method [50] to 3D cases, and in Section 4, we present a convergence analysis of this method. In Section 5, we define the shape optimization problem, which aims to find the maximal band gap in cubic lattices with silicon blocks and spheres. We also include numerical experiments in this section to verify our results. Finally, in Section 6, we provide a conclusion and potential future works.

2 Problem formulation

We recap in this section the derivation of the band function computation for 3D PhCs as a parametrized Maxwell eigenvalue problem (2.5) and its weak formulation (2.7). Then we describe its finite element discretization by the 𝐇(curl)\mathbf{H}(\text{curl})- conforming 𝐤\mathbf{k}-modified Nédélec edge element method (2.9). Finally, we provide an efficient approximation of the gradient of the band functions.

2.1 Maxwell equations, eigenvalue problem, and the weak formulation

In SI convention, the time harmonic Maxwell equations with 𝐄(𝐱,t):=𝐄(𝐱)eiωt\mathbf{E}(\mathbf{x},t):=\mathbf{E}(\mathbf{x})\mathrm{e}^{-i\omega t} and 𝐇(𝐱,t):=𝐇(𝐱)eiωt\mathbf{H}(\mathbf{x},t):=\mathbf{H}(\mathbf{x})\mathrm{e}^{-i\omega t} for linear, non-dispersive, and nonmagnetic media, with free charges and free currents, consist of a system of four equations [25]

×𝐄(𝐱)iωμ0𝐇(𝐱)\displaystyle\nabla\times\mathbf{E}(\mathbf{x})-i\omega\mu_{0}\mathbf{H}(\mathbf{x}) =0 in 3,\displaystyle=0\quad\text{ in }\mathbb{R}^{3}, (2.1a)
×𝐇(𝐱)+iωϵ0ϵ(𝐱)𝐄(𝐱)\displaystyle\nabla\times\mathbf{H}(\mathbf{x})+i\omega\epsilon_{0}\epsilon(\mathbf{x})\mathbf{E}(\mathbf{x}) =0 in 3,\displaystyle=0\quad\text{ in }\mathbb{R}^{3}, (2.1b)
(ϵ(𝐱)𝐄(𝐱))\displaystyle\nabla\cdot\left(\epsilon(\mathbf{x})\mathbf{E}(\mathbf{x})\right) =0 in 3,\displaystyle=0\quad\text{ in }\mathbb{R}^{3}, (2.1c)
𝐇(𝐱)\displaystyle\nabla\cdot\mathbf{H}(\mathbf{x}) =0 in 3,\displaystyle=0\quad\text{ in }\mathbb{R}^{3}, (2.1d)

where 𝐄\mathbf{E} is the electric field, 𝐇\mathbf{H} is the magnetic field, the scalar ω0\omega\geq 0 is the frequency of the electromagnetic wave, μ0\mu_{0} is the vacuum permeability, ϵ0\epsilon_{0} is the vacuum permittivity, and ϵL(3;+)\epsilon\in L^{\infty}(\mathbb{R}^{3};\mathbb{R}^{+}) is the relative permittivity.

Applying the curl operator to (2.1b) and using (2.1a) give the equations for the magnetic field

{×(ϵ(𝐱)1×𝐇(𝐱))(ωc1)2𝐇(𝐱)=0 in 3,𝐇(𝐱)=0 in 3,\left\{\begin{aligned} \nabla\times\left(\epsilon(\mathbf{x})^{-1}\nabla\times\mathbf{H}(\mathbf{x})\right)-\left(\omega c^{-1}\right)^{2}\mathbf{H}(\mathbf{x})&=0\qquad\text{ in }\mathbb{R}^{3},\\ \nabla\cdot\mathbf{H}(\mathbf{x})&=0\qquad\text{ in }\mathbb{R}^{3},\end{aligned}\right. (2.2)

with ϵ0μ0=c2\epsilon_{0}\mu_{0}=c^{-2}, where cc is the speed of light. The electric field 𝐄(𝐱)\mathbf{E}(\mathbf{x}) can be derived from (2.1b):

𝐄(𝐱)=iωϵ0ϵ(𝐱)×𝐇(𝐱),\mathbf{E}(\mathbf{x})=\frac{i}{\omega\epsilon_{0}\epsilon(\mathbf{x})}\nabla\times\mathbf{H}(\mathbf{x}), (2.3)

which implies (2.1c). The above two equations form the governing system of 3D PhCs.

Next, 3D periodic PhCs possess a discrete translational symmetry in 3\mathbb{R}^{3} [26], i.e., the relative permittivity ϵ(𝐱)\epsilon(\mathbf{x}) satisfies

ϵ(𝐱+c1𝐚1+c2𝐚2+c3𝐚3)=ϵ(𝐱),𝐱3 and c1,c2,c3.\epsilon(\mathbf{x}+c_{1}\mathbf{a}_{1}+c_{2}\mathbf{a}_{2}+c_{3}\mathbf{a}_{3})=\epsilon(\mathbf{x}),\quad\forall\mathbf{x}\in\mathbb{R}^{3}\text{ and }c_{1},c_{2},c_{3}\in\mathbb{Z}.

The primitive lattice vectors, denoted as 𝐚1,𝐚2,𝐚3\mathbf{a}_{1},\mathbf{a}_{2},\mathbf{a}_{3}, are the shortest possible vectors that satisfy this condition which span the unit cell Ω\Omega. For example, square lattice has primitive lattice vectors 𝐚i=aei\mathbf{a}_{i}=a{e}_{i} for i=1,2,3i=1,2,3, with aa\in\mathbb{R} being the lattice constant and (ei)i=1,2,3(e_{i})_{i=1,2,3} being the canonical basis in 3\mathbb{R}^{3}. Additionally, the reciprocal lattice vectors, 𝐛i\mathbf{b}_{i}, are defined by the property

𝐛i𝐚j=2πδij, for i,j=1,2,3,\mathbf{b}_{i}\cdot\mathbf{a}_{j}=2\pi\delta_{ij},\quad\text{ for }i,j=1,2,3, (2.4)

which generate the so-called reciprocal lattice. The elementary cell of the reciprocal lattice is the (first) Brillouin zone \mathcal{B}, i.e., the region closer to a certain lattice point than to any other lattice points in the reciprocal lattice. Note that if the materials in the unit cell have additional symmetry, e.g. mirror symmetry, we can further restrict 𝐤\mathbf{k} to the irreducible Brillouin zone (IBZ).

Based on Bloch’s theorem [28], the spectrum corresponding to a periodic structure in the full space is equivalent to the union of all spectra corresponding to the quasi-periodic problems in the unit cell. Thus, we apply Bloch’s theorem and aim to find the wave function 𝐇\mathbf{H} taking the form of a plane wave modulated by a periodic function. Mathematically, it can be written as 𝐇(𝐱)=ei𝐤𝐱𝐮(𝐱)\mathbf{H}(\mathbf{x})=e^{i\mathbf{k}\cdot\mathbf{x}}\mathbf{u}(\mathbf{x}), where 𝐮(𝐱)\mathbf{u}(\mathbf{x}) is a periodic function satisfying 𝐮(𝐱+𝐚𝐢)=𝐮(𝐱)\mathbf{u}(\mathbf{x}+\mathbf{a_{i}})=\mathbf{u}(\mathbf{x}) for i=1,2,3i=1,2,3 and 𝐤\mathbf{k} is the wave vector varying in the Brillouin zone \mathcal{B}. Thus, by Bloch’s theorem, the eigenvalue problem (2.2) reduces to

{(+i𝐤)×(ϵ(𝐱)1(+i𝐤)×𝐮(𝐱))λ𝐮(𝐱)=𝟎 in Ω,(+i𝐤)𝐮(𝐱)=0 in Ω,\left\{\begin{aligned} (\nabla+i\mathbf{k})\times\left(\epsilon(\mathbf{x})^{-1}(\nabla+i\mathbf{k})\times\mathbf{u}(\mathbf{x})\right)-\lambda\mathbf{u}(\mathbf{x})&=\mathbf{0}\qquad\text{ in }\Omega,\\ (\nabla+i\mathbf{k})\cdot\mathbf{u}(\mathbf{x})&=0\qquad\text{ in }\Omega,\end{aligned}\right. (2.5)

where λ=(ωc1)2\lambda=\left(\omega c^{-1}\right)^{2}, 𝐤\mathbf{k} varies in the Brillouin zone \mathcal{B}, and 𝐮(𝐱)\mathbf{u}(\mathbf{x}) satisfies the periodic boundary conditions 𝐮(𝐱)=𝐮(𝐱+𝐚𝐣)\mathbf{u}(\mathbf{x})=\mathbf{u}(\mathbf{x}+\mathbf{a_{j}}) with 𝐚𝐣\mathbf{a_{j}} being the primitive lattice vector for j=1,2,3j=1,2,3. For all 𝐤\mathbf{k}\in\mathcal{B}, we can enumerate these eigenvalues in a non-decreasing manner (with their multiplicities counted) as

0λ1(𝐤)λ2(𝐤)λn(𝐤).\displaystyle 0\leq\lambda_{1}(\mathbf{k})\leq\lambda_{2}(\mathbf{k})\leq\cdots\leq\lambda_{n}(\mathbf{k})\leq\cdots\leq\infty.

Then {λn(𝐤)}n=1\{\lambda_{n}(\mathbf{k})\}_{n=1}^{\infty} is an infinite sequence with λn(𝐤)\lambda_{n}(\mathbf{k}) being a continuous function with respect to the wave vector 𝐤\mathbf{k} and λn(𝐤)\lambda_{n}(\mathbf{k})\to\infty when nn\to\infty [21].

Below we state the regularity of band functions, which can be proved in a similar manner as the 2D case [51]. Here, Lip()\mathrm{Lip}(\mathcal{B}) is the space of Lipschitz continuous functions in the Brillouin zone \mathcal{B} and Å()\mathring{A}(\mathcal{B}) denotes the space of piecewise analytic functions with a zero Lebesgue measure of singular point set composed of points of degeneracy (i.e., the set of points at which certain band functions intersect, with zero Lebesgue measure). Throughout, the set of singular points is denoted by 𝐒\mathbf{S}.

Theorem 2.1 (Piecewise analyticity and Lipschitz continuity of band functions).

For 3D periodic PhCs, λn(𝐤)Lip()Å()\lambda_{n}(\mathbf{k})\in\mathrm{Lip}(\mathcal{B})\cap\mathring{A}(\mathcal{B}) for all n+n\in\mathbb{N}^{+}.

Next, we introduce the weak formulation to (2.5). We first introduce useful notation for function spaces. For an open, bounded and simply-connected Lipschitz domain Ω3\Omega\subset\mathbb{R}^{3}, we define the space of square integrable functions L2(Ω)L^{2}(\Omega) equipped with the inner product (u,v)=Ωu(𝐱)v¯(𝐱)d𝐱(u,v)=\int_{\Omega}u(\mathbf{x})\cdot\bar{v}(\mathbf{x})\,\mathrm{d}\mathbf{x} and the corresponding norm by u=(Ω|u(𝐱)|2d𝐱)12\|u\|=\left(\int_{\Omega}|u(\mathbf{x})|^{2}\,\mathrm{d}\mathbf{x}\right)^{\frac{1}{2}}, where ¯\mathbf{\bar{\cdot}} denotes the complex conjugate. We denote by H1(Ω)H^{1}(\Omega) the usual Sobolev space with norm

fH1:=(|α|1α𝐱αfΩ2)1/2.\displaystyle\|f\|_{H^{1}}:=\bigg{(}\sum_{|\mathbf{\alpha}|\leq 1}\Big{\|}\frac{\partial^{\mathbf{\alpha}}}{\partial\mathbf{x}^{\mathbf{\alpha}}}f\Big{\|}^{2}_{\Omega}\bigg{)}^{1/2}.

We also write (,)(\cdot,\cdot), \|\cdot\| and (,)𝐇1(\cdot,\cdot)_{\mathbf{H}^{1}}, 𝐇1\|\cdot\|_{\mathbf{H}^{1}} for the inner products and norms of vector-valued functions spaces L2(Ω)3L^{2}(\Omega)^{3}, H1(Ω)3H^{1}(\Omega)^{3}, respectively. Let (𝐮,𝐯)𝐇(curl;Ω)=(𝐮,𝐯)+(×𝐮,×𝐯)(\mathbf{u},\mathbf{v})_{\mathbf{H}(\text{curl};\Omega)}=(\mathbf{u},\mathbf{v})+(\nabla\times\mathbf{u},\nabla\times\mathbf{v}) be the standard inner product on the Sobolev space 𝐇(curl;Ω):={𝐯L2(Ω)3:×𝐯L2(Ω)3}\mathbf{H}(\text{curl};\Omega):=\{\mathbf{v}\in L^{2}(\Omega)^{3}:\nabla\times\mathbf{v}\in L^{2}(\Omega)^{3}\} and (𝐮,𝐯)𝐇(div;Ω)=(𝐮,𝐯¯)+(𝐮,𝐯)(\mathbf{u},\mathbf{v})_{\mathbf{H}(\text{div};\Omega)}=(\mathbf{u},\overline{\mathbf{v}})+(\nabla\cdot\mathbf{u},\nabla\cdot\mathbf{v}) be the inner product on 𝐇(div;Ω):={𝐯L2(Ω)3:𝐯L2(Ω)3}\mathbf{H}(\text{div};\Omega):=\{\mathbf{v}\in L^{2}(\Omega)^{3}:\nabla\cdot\mathbf{v}\in L^{2}(\Omega)^{3}\}. Also we will use the following spaces for the weak formulation:

C(Ω)\displaystyle C^{\infty}({\Omega}) ={v:Ω:Dαv exists ,multi-indices α},\displaystyle=\{v:{\Omega}\to\mathbb{C}:D^{\mathbf{\alpha}}v\text{ exists },\forall\text{multi-indices }\mathbf{\alpha}\},
Cper(Ω)\displaystyle C^{\infty}_{\text{per}}({\Omega}) ={vC(Ω):v(𝐱+𝐚i)=v(𝐱),i=1,2,3,𝐱,𝐱+𝐚iΩ},\displaystyle=\{v\in C^{\infty}({\Omega}):v(\mathbf{x}+\mathbf{a}_{i})=v(\mathbf{x}),\forall i=1,2,3,\forall\mathbf{x},\mathbf{x}+\mathbf{a}_{i}\in\partial\Omega\},
Cdiv(Ω¯)\displaystyle C^{\infty}_{\text{div}}(\bar{\Omega}) ={𝐯C(Ω¯)3:𝐯(𝐱+𝐚i)𝐧=𝐯(𝐱)𝐧,i=1,2,3,𝐱,𝐱+𝐚iΩ},\displaystyle=\{\mathbf{v}\in C^{\infty}(\bar{\Omega})^{3}:\mathbf{v}(\mathbf{x}+\mathbf{a}_{i})\cdot\mathbf{n}=-\mathbf{v}(\mathbf{x})\cdot\mathbf{n},\forall i=1,2,3,\forall\mathbf{x},\mathbf{x}+\mathbf{a}_{i}\in\partial\Omega\},
Ccurl(Ω)\displaystyle C^{\infty}_{\text{curl}}({\Omega}) ={𝐯C(Ω)3:𝐯(𝐱+𝐚i)×𝐧=𝐯(𝐱)×𝐧,i=1,2,3,𝐱,𝐱+𝐚iΩ}.\displaystyle=\{\mathbf{v}\in C^{\infty}({\Omega})^{3}:\mathbf{v}(\mathbf{x}+\mathbf{a}_{i})\times\mathbf{n}=-\mathbf{v}(\mathbf{x})\times\mathbf{n},\forall i=1,2,3,\forall\mathbf{x},\mathbf{x}+\mathbf{a}_{i}\in\partial\Omega\}.

Here 𝐧\mathbf{n} is the unit outward normal vector to Ω\partial\Omega. The periodic Sobolev spaces Hper1(Ω)H_{\text{per}}^{1}(\Omega), 𝐇per(div;Ω)\mathbf{H}_{\text{per}}(\text{div};\Omega), 𝐇per(curl;Ω)\mathbf{H}_{\text{per}}(\text{curl};\Omega) are the closures of the spaces Cper(Ω)C^{\infty}_{\text{per}}({\Omega}), Cdiv(Ω)C^{\infty}_{\text{div}}({\Omega}), Ccurl(Ω)C^{\infty}_{\text{curl}}({\Omega}) with respect to the Sobolev space norms H1\|\cdot\|_{H^{1}},𝐇(div;Ω)\|\cdot\|_{\mathbf{H}(\text{div};\Omega)}, 𝐇(curl;Ω)\|\cdot\|_{\mathbf{H}(\text{curl};\Omega)} induced by the inner product mentioned above respectively. The periodic space Lper2(Ω)L^{2}_{\text{per}}(\Omega) is the closure of the space Cper(Ω)C^{\infty}_{\text{per}}({\Omega}) with respect to the norm \|\cdot\|.

Then the spaces for the weak formulation are given by

Q:=Hper1(Ω)and𝐕:=𝐇per(curl;Ω).Q:=H_{\text{per}}^{1}(\Omega)\quad\mbox{and}\quad\mathbf{V}:=\mathbf{H}_{\text{per}}(\text{curl};\Omega). (2.6)

For 𝐮,𝐯𝐕\mathbf{u},\mathbf{v}\in\mathbf{V} and qQq\in Q, we define the sesquilinear forms

a(𝐮,𝐯;𝐤)\displaystyle a(\mathbf{u},\mathbf{v};\mathbf{k}) :=Ωϵ(𝐱)1(+i𝐤)×𝐮(+i𝐤)×𝐯¯d𝐱,\displaystyle:=\int_{\Omega}\epsilon(\mathbf{x})^{-1}(\nabla+i\mathbf{k})\times\mathbf{u}\cdot\overline{(\nabla+i\mathbf{k})\times\mathbf{v}}\,\mathrm{d}\mathbf{x},
b(q,𝐮;𝐤)\displaystyle b(q,\mathbf{u};\mathbf{k}) :=Ω(+i𝐤)q𝐮¯d𝐱,\displaystyle:=-\int_{\Omega}(\nabla+i\mathbf{k})q\cdot\bar{\mathbf{u}}\,\mathrm{d}\mathbf{x},
(𝐮,𝐯)\displaystyle(\mathbf{u},\mathbf{v}) :=Ω𝐮𝐯¯d𝐱.\displaystyle:=\int_{\Omega}\mathbf{u}\cdot\bar{\mathbf{v}}\,\mathrm{d}\mathbf{x}.

Then the variational formulation to (2.5) can be written in a mixed form: for a given 𝐤\mathbf{k}\in\mathcal{B}, find non-trivial eigentriplets {(λn,𝐮n,pn)}n=1(,𝐕,Q)\{(\lambda_{n},\mathbf{u}_{n},p_{n})\}_{n=1}^{\infty}\subset(\mathbb{R},\mathbf{V},Q) such that

{a(𝐮n,𝐯;𝐤)+b(pn,𝐯;𝐤)=λn(𝐮n,𝐯),v𝐕,b(q,𝐮n;𝐤)¯=0,qQ.\left\{\begin{aligned} a(\mathbf{u}_{n},\mathbf{v};\mathbf{k})+b(p_{n},\mathbf{v};\mathbf{k})&=\lambda_{n}(\mathbf{u}_{n},\mathbf{v}),&\forall v\in\mathbf{V},\\ \overline{b(q,\mathbf{u}_{n};\mathbf{k})}&=0,&\forall q\in Q.\end{aligned}\right. (2.7)

Finally, we introduce a regularity assumption for the finite element analysis [4, Assumption H0\operatorname{H}_{0}],

Assumption 2.1.

There exists s>1/2s>1/2 such that 𝐕𝐇per(div;Ω)\mathbf{V}\cap\mathbf{H}_{\mathrm{per}}(\mathrm{div};\Omega) is continuously embedded in Hs(Ω)3H^{s}(\Omega)^{3}. Moreover, for a given 𝐤\mathbf{k}\in\mathcal{B} and for any 𝐟L2(Ω)3\mathbf{f}\in L^{2}(\Omega)^{3}, let (𝐮,p)(𝐕,Q)(\mathbf{u},p)\in(\mathbf{V},Q) satisfy

{a(𝐮,𝐯;𝐤)+b(p,𝐯;𝐤)=(𝐟,𝐯),v𝐕,b(q,𝐮;𝐤)¯=0,qQ.\left\{\begin{aligned} a(\mathbf{u},\mathbf{v};\mathbf{k})+b(p,\mathbf{v};\mathbf{k})&=(\mathbf{f},\mathbf{v}),&\forall v\in\mathbf{V},\\ \overline{b(q,\mathbf{u};\mathbf{k})}&=0,&\forall q\in Q.\end{aligned}\right.

Then there is s>1/2s>1/2 and r(0,1/2)r\in(0,1/2), such that 𝐮Hs(Ω)3\mathbf{u}\in H^{s}(\Omega)^{3} and ×𝐮Hr(Ω)3\nabla\times\mathbf{u}\in H^{r}(\Omega)^{3}.

2.2 𝐇(curl)\mathbf{H}(\text{curl})- conforming 𝐤\mathbf{k}-modified Nédélec edge FEM

In this section we describe the discretization of problem (2.7) for each wave vector 𝐤\mathbf{k}\in\mathcal{B} using the 𝐇(curl)\mathbf{H}(\text{curl})- conforming 𝐤\mathbf{k}-modified Nédélec edge element method, which is stable with respect to 𝐤\mathbf{k}. Since the divergence of the curl of any vector is zero, taking the divergence on both sides of the first equation in (2.5) gives its second equation. Furthermore, the zero eigenvalue of (2.5) has an infinite multiplicity since the curl operator has an infinite-dimensional kernel. Thus, a discretization of the eigenvalue problem (2.5) solely using the scalar node-based FEM may contain non-physical solutions (i.e., spurious modes [48]). Indeed, most eigenvalue solvers are inefficient when dealing with such eigenvalue problems [2].

To eliminate these spurious modes, the standard approach is to use the edge FEM and add the constraint (2.5) explicitly [31, 48]. The Lagrange multiplier in the mixed form in (2.7) is one common approach to add the divergence constraint to the system. Also, several methods have been proposed to eliminate this large null space [14, 11, 24, 38, 36, 35], including the special 𝐇(curl)\mathbf{H}(\text{curl})-conforming 𝐤\mathbf{k}-modified Nédélec edge elements [15, 4] and the mixed discontinuous Galerkin (DG) formulation with modified Nédélec basis functions [35]. The 𝐇(curl)\mathbf{H}(\text{curl})- conforming 𝐤\mathbf{k}-modified Nédélec edge element method was first proposed and analyzed in [14, 15] under strong regularity restrictions and in the case of uniform mesh sequences, with an improved analysis under minimal assumptions on the regularity of the eigensolutions and on the mesh sequences [4]. We recap the Nédélec edge element method below.

Let 𝒯h\mathcal{T}_{h} be a periodic, shape-regular, conformal tetrahedral mesh for Ω\Omega aligned with the possible discontinuities of α,β\alpha,\beta, where the phrase “periodic” means that the meshes are the same on each pair of the boundary. Let h=maxK𝒯hhKh=\max_{K\in\mathcal{T}_{h}}h_{K}, where hKh_{K} is the diameter of KK. We denote the set of all edges by \mathcal{E} and the set of all vertexes by 𝒱\mathcal{V}. The conforming finite element spaces of (2.6) are [4]

Qh𝐤\displaystyle Q^{\mathbf{k}}_{h} :={qQ:q|K=ei𝐤𝐱q~,q~Pk+1(K) and K𝒯h},\displaystyle:=\{q\in Q:q|_{K}=e^{-i\mathbf{k}\cdot\mathbf{x}}\tilde{q},\;\quad\forall\tilde{q}\in P_{k+1}(K)\text{ and }\forall K\in\mathcal{T}_{h}\}, (2.8)
𝐕h𝐤\displaystyle\mathbf{V}^{\mathbf{k}}_{h} :={𝐯𝐕:𝐯|K=ei𝐤𝐱𝐯~,𝐯~Sk(K) and K𝒯h},\displaystyle:=\{\mathbf{v}\in\mathbf{V}:\mathbf{v}|_{K}=e^{-i\mathbf{k}\cdot\mathbf{x}}\tilde{\mathbf{v}},\quad\forall\tilde{\mathbf{v}}\in S_{k}(K)\;\;\text{ and }\forall K\in\mathcal{T}_{h}\},

where Pk(K)P_{k}(K) is the space of local polynomials of total degree less than or equal to kk on KK, and Sk(K)S_{k}(K) is the space of vector polynomials with the form 𝐚(𝐱)+𝐛(𝐱)×𝐱\mathbf{a}(\mathbf{x})+\mathbf{b}(\mathbf{x})\times\mathbf{x} with 𝐚,𝐛Pk(K)3\mathbf{a},\mathbf{b}\in P_{k}(K)^{3}.

Given 𝐤\mathbf{k}\in\mathcal{B}, the discretization of problem (2.7) reads: find a non-trivial eigentriplet (λn,h,𝐮n,h,pn,h)(,𝐕h𝐤,Qh𝐤)(\lambda_{n,h},\mathbf{u}_{n,h},p_{n,h})\in(\mathbb{R},\mathbf{V}_{h}^{\mathbf{k}},Q_{h}^{\mathbf{k}}) with (𝐮n,h,λn,h)(𝟎,0)(\mathbf{u}_{n,h},\lambda_{n,h})\neq(\mathbf{0},0), such that

{a(𝐮n,h,𝐯;𝐤)+b(pn,h,𝐯;𝐤)=λn,h(𝐮n,h,𝐯),𝐯𝐕h𝐤,b(q,𝐮n,h;𝐤)¯=0,qQh𝐤.\left\{\begin{aligned} a(\mathbf{u}_{n,h},\mathbf{v};\mathbf{k})+b(p_{n,h},\mathbf{v};\mathbf{k})&=\lambda_{n,h}(\mathbf{u}_{n,h},\mathbf{v}),\quad\forall\mathbf{v}\in\mathbf{V}_{h}^{\mathbf{k}},\\ \overline{b(q,\mathbf{u}_{n,h};\mathbf{k})}&=0,\qquad\qquad\qquad\forall q\in Q_{h}^{\mathbf{k}}.\end{aligned}\right. (2.9)

For 𝐤=𝟎\mathbf{k}=\mathbf{0}, one should make the spaces 𝐕\mathbf{V}, QQ and their corresponding FEM spaces 𝐕h𝟎\mathbf{V}^{\mathbf{0}}_{h}, Qh𝟎Q^{\mathbf{0}}_{h} orthogonal to constant vectors or constants in order to guarantee the stability of (2.7) and (2.9) [5].

Theorem 2.2 ([4, Theorem 3]).

Let λn\lambda_{n} be an eigenvalue of problem (2.7) with multiplicity mm. Then there are mm discrete eigenvalues λn1,h,,λnm,h\lambda_{n_{1},h},\cdots,\lambda_{n_{m},h} of problem (2.9) converging to λ\lambda. Moreover, with λ^n:=1mi=1mλni,h\hat{\lambda}_{n}:=\frac{1}{m}\sum_{i=1}^{m}\lambda_{n_{i},h}, there is h0h_{0} such that for any 0<h<h00<h<h_{0} there holds

|λnλ^n|Ch2r,|\lambda_{n}-\hat{\lambda}_{n}|\leq Ch^{2r}, (2.10)

where C>0C>0 is a constant and rr is defined in Assumption 2.1.

2.3 Gradient of λn(𝐤)\lambda_{n}(\mathbf{k}) in the parameter domain \mathcal{B}

Next, we derive the formula of the gradient jλn:=λnkj\partial_{j}\lambda_{n}:=\frac{\partial\lambda_{n}}{\partial k_{j}} for n+n\in\mathbb{N}_{+} and j=1,2,3j=1,2,3, and provide its numerical approximation. Recall that (ej)j=1,2,3(e_{j})_{j=1,2,3} denotes the canonical basis in 3\mathbb{R}^{3}.

Theorem 2.3.

Let the non-trivial eigentriplet (λn(𝐤),𝐮n(𝐤),p(𝐤))(,𝐕,Q)(\lambda_{n}(\mathbf{k}),\mathbf{u}_{n}(\mathbf{k}),p(\mathbf{k}))\in(\mathbb{R},\mathbf{V},Q) be the solution to the weak formulation (2.7) for any 𝐤\mathbf{k}\in\mathcal{B}. Then there holds

jλn=2(Ωϵ1(ej×𝐮n)(+i𝐤)×𝐮n¯d𝐱),j=1,2,3.\displaystyle\partial_{j}\lambda_{n}=-2\Im\Big{(}\int_{\Omega}\epsilon^{-1}(e_{j}\times\mathbf{u}_{n})\cdot\overline{(\nabla+i\mathbf{k})\times\mathbf{u}_{n}}\,\mathrm{d}\mathbf{x}\Big{)},\quad j=1,2,3. (2.11)
Proof.

We abbreviate λn(𝐤)\lambda_{n}(\mathbf{k}) and 𝐮n(𝐤)\mathbf{u}_{n}(\mathbf{k}) to λ\lambda and 𝐮\mathbf{u}. By definition, for all 𝐯𝐕\mathbf{v}\in\mathbf{V}, there holds

a(𝐮,𝐯;𝐤)\displaystyle a(\mathbf{u},\mathbf{v};\mathbf{k}) =Ωϵ1(+i𝐤)×𝐮(+i𝐤)×𝐯¯d𝐱\displaystyle=\int_{\Omega}\epsilon^{-1}(\nabla+i\mathbf{k})\times\mathbf{u}\cdot\overline{(\nabla+i\mathbf{k})\times\mathbf{v}}\,\mathrm{d}\mathbf{x}
=Ωϵ1(+i𝐤)×𝐮(i𝐤)×𝐯¯d𝐱\displaystyle=\int_{\Omega}\epsilon^{-1}(\nabla+i\mathbf{k})\times\mathbf{u}\cdot(\nabla-i\mathbf{k})\times\overline{\mathbf{v}}\,\mathrm{d}\mathbf{x}
=Ωϵ1(×𝐮+i𝐤×𝐮)(×𝐯¯i𝐤×𝐯¯)d𝐱\displaystyle=\int_{\Omega}\epsilon^{-1}(\nabla\times\mathbf{u}+i\mathbf{k}\times\mathbf{u})\cdot(\nabla\times\overline{\mathbf{v}}-i\mathbf{k}\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x}
=Ωϵ1(×𝐮)(×𝐯¯)d𝐱Ωϵ1(×𝐮)(i𝐤×𝐯¯)d𝐱\displaystyle=\int_{\Omega}\epsilon^{-1}(\nabla\times\mathbf{u})\cdot(\nabla\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x}-\int_{\Omega}\epsilon^{-1}(\nabla\times\mathbf{u})\cdot(i\mathbf{k}\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x}
+Ωϵ1(i𝐤×𝐮)(×𝐯¯)d𝐱+Ωϵ1(𝐤×𝐮)(𝐤×𝐯¯)d𝐱.\displaystyle\quad+\int_{\Omega}\epsilon^{-1}(i\mathbf{k}\times\mathbf{u})\cdot(\nabla\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x}+\int_{\Omega}\epsilon^{-1}(\mathbf{k}\times\mathbf{u})\cdot(\mathbf{k}\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x}.

Upon utilizing the formula for j=1,2,3j=1,2,3,

j(𝐤×𝐰)=ej×𝐰+𝐤×j𝐰,𝐰𝐕,\partial_{j}(\mathbf{k}\times\mathbf{w})=e_{j}\times\mathbf{w}+\mathbf{k}\times\partial_{j}\mathbf{w},\quad\forall\mathbf{w}\in\mathbf{V},

and taking the partial derivative of (2.7) with respect to kjk_{j}, we obtain

a(j𝐮,𝐯;𝐤)λ(j𝐮,𝐯)=f(𝐯),a(\partial_{j}\mathbf{u},\mathbf{v};\mathbf{k})-\lambda(\partial_{j}\mathbf{u},\mathbf{v})=f(\mathbf{v}),

with f(𝐯;𝐤):=a2j(𝐮,𝐯)a3j(𝐮,𝐯)a4j(𝐮,𝐯;𝐤)+jλ(𝐮,𝐯)f(\mathbf{v};\mathbf{k}):=-a_{2}^{j}(\mathbf{u},\mathbf{v})-a_{3}^{j}(\mathbf{u},\mathbf{v})-a_{4}^{j}(\mathbf{u},\mathbf{v};\mathbf{k})+\partial_{j}\lambda(\mathbf{u},\mathbf{v}), where a2j(,),a3j(,)a_{2}^{j}(\cdot,\cdot),a_{3}^{j}(\cdot,\cdot) and a4j(,)a_{4}^{j}(\cdot,\cdot) are defined as

a2j(𝐮,𝐯)\displaystyle a_{2}^{j}(\mathbf{u},\mathbf{v}) :=Ωϵ1(×𝐮)(iej×𝐯¯)d𝐱,\displaystyle:=-\int_{\Omega}\epsilon^{-1}(\nabla\times\mathbf{u})\cdot(ie_{j}\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x},
a3j(𝐮,𝐯)\displaystyle a_{3}^{j}(\mathbf{u},\mathbf{v}) :=Ωϵ1(iej×𝐮)(×𝐯¯)d𝐱,\displaystyle:=\int_{\Omega}\epsilon^{-1}(ie_{j}\times\mathbf{u})\cdot(\nabla\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x},
a4j(𝐮,𝐯;𝐤)\displaystyle a_{4}^{j}(\mathbf{u},\mathbf{v};\mathbf{k}) :=Ωϵ1(ej×𝐮)(𝐤×𝐯¯)d𝐱+Ωϵ1(𝐤×𝐮)(ej×𝐯¯)d𝐱.\displaystyle:=\int_{\Omega}\epsilon^{-1}(e_{j}\times\mathbf{u})\cdot(\mathbf{k}\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x}+\int_{\Omega}\epsilon^{-1}(\mathbf{k}\times\mathbf{u})\cdot(e_{j}\times\overline{\mathbf{v}})\,\mathrm{d}\mathbf{x}.

By repeating the argument for [51, Theorem 3.6], we can derive the derivatives of the eigenvalue λ\lambda with respect to 𝐤\mathbf{k} through f(𝐮)=0f(\mathbf{u})=0, and arrive at

jλ=a2j(𝐮,𝐮)+a3j(𝐮,𝐮)+a4j(𝐮,𝐮;𝐤),\partial_{j}\lambda=a_{2}^{j}(\mathbf{u},\mathbf{u})+a_{3}^{j}(\mathbf{u},\mathbf{u})+a_{4}^{j}(\mathbf{u},\mathbf{u};\mathbf{k}), (2.12)

since the eigenfunction is normalized, i.e., (𝐮,𝐮)=1(\mathbf{u},\mathbf{u})=1. Following equation (2.12), we find

jλ\displaystyle\partial_{j}\lambda =Ωϵ1((×𝐮)(iej×𝐮¯)+(iej×𝐮)(×𝐮¯)+(ej×𝐮)(𝐤×𝐮¯)+(𝐤×𝐮)(ej×𝐮¯))d𝐱\displaystyle=\int_{\Omega}\epsilon^{-1}\left(-(\nabla\times\mathbf{u})\cdot(ie_{j}\times\overline{\mathbf{u}})+(ie_{j}\times\mathbf{u})\cdot(\nabla\times\overline{\mathbf{u}})+(e_{j}\times\mathbf{u})\cdot(\mathbf{k}\times\overline{\mathbf{u}})+(\mathbf{k}\times\mathbf{u})\cdot(e_{j}\times\overline{\mathbf{u}})\right)\,\mathrm{d}\mathbf{x}
=Ωϵ1((ej×𝐮)(i+𝐤)×𝐮¯+(ej×𝐮¯)(i+𝐤)×𝐮)d𝐱\displaystyle=\int_{\Omega}\epsilon^{-1}\left((e_{j}\times\mathbf{u})\cdot(i\nabla+\mathbf{k})\times\overline{\mathbf{u}}+(e_{j}\times\overline{\mathbf{u}})\cdot(-i\nabla+\mathbf{k})\times\mathbf{u}\right)\,\mathrm{d}\mathbf{x}
=Ωϵ1((ei×𝐮)(i+𝐤)×𝐮¯+(ei×𝐮)(i+𝐤)×𝐮¯¯)d𝐱\displaystyle=\int_{\Omega}\epsilon^{-1}\left((e_{i}\times\mathbf{u})\cdot(i\nabla+\mathbf{k})\times\overline{\mathbf{u}}+\overline{(e_{i}\times\mathbf{u})\cdot(i\nabla+\mathbf{k})\times\overline{\mathbf{u}}}\right)\,\mathrm{d}\mathbf{x}
=2(Ωϵ1(ej×𝐮)(i+𝐤)×𝐮¯d𝐱)\displaystyle=2\Re\left(\int_{\Omega}\epsilon^{-1}(e_{j}\times\mathbf{u})\cdot(i\nabla+\mathbf{k})\times\overline{\mathbf{u}}\,\mathrm{d}\mathbf{x}\right)
=2(Ωϵ1(ej×𝐮)(+i𝐤)×𝐮¯d𝐱).\displaystyle=-2\Im\left(\int_{\Omega}\epsilon^{-1}(e_{j}\times\mathbf{u})\cdot\overline{(\nabla+i\mathbf{k})\times\mathbf{u}}\,\mathrm{d}\mathbf{x}\right).

This completes the proof. ∎

The use of modified basis functions (2.8) greatly simplifies the computation of jλn\partial_{j}\lambda_{n}, and hence reduce the computational complexity. For example, for the lowest order Nédélec FEM space with k=0k=0 in (2.8), the degrees of freedom for 𝐕h𝐤\mathbf{V}_{h}^{\mathbf{k}} are

e,𝐤(𝐯):=𝐱e𝐲eei𝐤(x𝐳e)𝐯𝐭eds,for all e and 𝐯𝐕h𝐤.\ell_{e,\mathbf{k}}(\mathbf{v}):=\int_{\mathbf{x}_{e}}^{\mathbf{y}_{e}}e^{i\mathbf{k}\cdot(x-\mathbf{z}_{e})}\mathbf{v}\cdot\mathbf{t}_{e}\,\mathrm{d}s,\quad\text{for all }e\in\mathcal{E}\text{ and }\mathbf{v}\in\mathbf{V}_{h}^{\mathbf{k}}.

Here, 𝐳e:=(𝐱e+𝐲e)/2\mathbf{z}_{e}:=(\mathbf{x}_{e}+\mathbf{y}_{e})/2 denotes the midpoint of edge ee\in\mathcal{E}, identified with the ordered vertices (𝐱e,𝐲e)(\mathbf{x}_{e},\mathbf{y}_{e}), and 𝐭e:=(𝐲e𝐱e)/𝐲e𝐱e\mathbf{t}_{e}:=(\mathbf{y}_{e}-\mathbf{x}_{e})/\|\mathbf{y}_{e}-\mathbf{x}_{e}\| represents the unit tangent vector of ee. Hence, 𝐕h𝐤\mathbf{V}_{h}^{\mathbf{k}} of the lowest order can be expressed as

𝐕h𝐤:=span{ψe,𝐤=ei𝐤(x𝐳e)ψe,𝟎:e},\mathbf{V}_{h}^{\mathbf{k}}:=\text{span}\{\psi_{e,\mathbf{k}}=e^{-i\mathbf{k}\cdot(x-\mathbf{z}_{e})}\psi_{e,\mathbf{0}}:e\in\mathcal{E}\},

with e,𝐤(ψe,𝐤)=1\ell_{e,\mathbf{k}}(\psi_{e,\mathbf{k}})=1 and e,𝟎(ψe,𝟎)=1\ell_{e,\mathbf{0}}(\psi_{e,\mathbf{0}})=1. Similarly, Qh𝐤Q_{h}^{\mathbf{k}} of the lowest-order can be represented by

Qh𝐤:=span{qv,𝐤=ei𝐤(x𝐱v)qv,𝟎:v𝒱},\displaystyle Q_{h}^{\mathbf{k}}:=\text{span}\{q_{v,\mathbf{k}}=e^{-i\mathbf{k}\cdot(x-\mathbf{x}_{v})}q_{v,\mathbf{0}}:v\in\mathcal{V}\},

with v,𝐤(qv,𝐤)=1\ell_{v,\mathbf{k}}(q_{v,\mathbf{k}})=1 and v,𝟎(qv,𝟎)=1\ell_{v,\mathbf{0}}(q_{v,\mathbf{0}})=1, which has the vertex-based degrees of freedom defined as v,𝐤(q):=ei𝐤(x𝐱v)q(𝐱v)\ell_{v,\mathbf{k}}(q):=e^{i\mathbf{k}\cdot(x-\mathbf{x}_{v})}q(\mathbf{x}_{v}), for all v𝒱v\in\mathcal{V}, 𝐱v\mathbf{x}_{v} is the coordinate of a vertex in 𝒱\mathcal{V}.

With the basis functions defined above, we obtain

a(ψe1,𝐤,ψe2,𝐤;𝐤)\displaystyle a(\psi_{e_{1},\mathbf{k}},\psi_{e_{2},\mathbf{k}};\mathbf{k}) =ei𝐤(𝐳e1𝐳e2)Ωϵ1×ψe1,𝟎×ψe2,𝟎¯d𝐱,\displaystyle=e^{i\mathbf{k}\cdot(\mathbf{z}_{e_{1}}-\mathbf{z}_{e_{2}})}\int_{\Omega}\epsilon^{-1}\nabla\times\psi_{e_{1},\mathbf{0}}\cdot\overline{\nabla\times\psi_{e_{2},\mathbf{0}}}\,\mathrm{d}\mathbf{x},
b(qv,𝐤,ψe,𝐤;𝐤)\displaystyle b(q_{v,\mathbf{k}},\psi_{e,\mathbf{k}};\mathbf{k}) :=ei𝐤(𝐱v𝐳e)Ωqv,𝟎ψe,𝟎¯d𝐱,\displaystyle:=-e^{i\mathbf{k}\cdot(\mathbf{x}_{v}-\mathbf{z}_{e})}\int_{\Omega}\nabla q_{v,\mathbf{0}}\cdot\overline{\psi_{e,\mathbf{0}}}\,\mathrm{d}\mathbf{x},
(ψe1,𝐤,ψe2,𝐤)\displaystyle(\psi_{e_{1},\mathbf{k}},\psi_{e_{2},\mathbf{k}}) :=ei𝐤(𝐳e1𝐳e2)Ωψe1,𝟎ψe2,𝟎¯d𝐱.\displaystyle:=e^{i\mathbf{k}\cdot(\mathbf{z}_{e_{1}}-\mathbf{z}_{e_{2}})}\int_{\Omega}\psi_{e_{1},\mathbf{0}}\cdot\overline{\psi_{e_{2},\mathbf{0}}}\,\mathrm{d}\mathbf{x}.

Together with (2.11), we obtain

jλn,h=Ωϵ1(ei×ψe1,𝐤)(+i𝐤)×ψe2,𝐤¯d𝐱\displaystyle\partial_{j}\lambda_{n,h}=\int_{\Omega}\epsilon^{-1}(e_{i}\times\psi_{e_{1},\mathbf{k}})\cdot\overline{(\nabla+i\mathbf{k})\times\psi_{e_{2},\mathbf{k}}}\,\mathrm{d}\mathbf{x} =ei𝐤(𝐳e1𝐳e2)Ωϵ1(ei×ψe1,𝟎)×ψe2,𝟎¯d𝐱.\displaystyle=e^{i\mathbf{k}\cdot(\mathbf{z}_{e_{1}}-\mathbf{z}_{e_{2}})}\int_{\Omega}\epsilon^{-1}(e_{i}\times\psi_{e_{1},\mathbf{0}})\cdot\overline{\nabla\times\psi_{e_{2},\mathbf{0}}}\,\mathrm{d}\mathbf{x}.

Thus, the use of the modified basis functions separates the integral with the parameter 𝐤\mathbf{k}, transforming it into a standalone exponential function during the computation. This separation streamlines evaluating the integrals, making it a conventional integration involving parameter-free polynomials.

3 hphp-adaptive sampling method in the parameter domain \mathcal{B}

In this section, we generalize the hphp-adaptive sampling method [50] from 2D PhCs to 3D PhCs for the purpose of computing the \ellth and +1\ell+1th band functions with 11\leq\ell\in\mathbb{N}. We introduce in Section 3.1 an adaptive mesh in the parameter domain \mathcal{B} which refines the elements containing singularity points, and propose in Section 3.2 an element-wise interpolation operator to reconstruct the target band functions.

3.1 Adaptive mesh generation

We first recall the definition of a regular mesh without hanging nodes [45]. Let 3\mathcal{B}\subset\mathbb{R}^{3} be a polygon with straight sides and a mesh 𝒯={Ti}i=1M\mathcal{T}=\{T_{i}\}_{i=1}^{M} be a partition of \mathcal{B} into MM open, disjoint tetrahedrons TiT_{i} with D¯=i=1MTi¯\overline{D}=\bigcup_{i=1}^{M}\overline{T_{i}}. For each T𝒯T\in\mathcal{T}, we denote by hTh_{T} its diameter and ρT\rho_{T} the diameter of its largest inscribed ball.

Definition 3.1.

A mesh 𝒯={Ti}i=1M\mathcal{T}=\{T_{i}\}_{i=1}^{M} is said to be a regular mesh without hanging nodes if it satisfies the following conditions. (i) For iji\neq j, Ti¯Tj¯\overline{T_{i}}\bigcap\overline{T_{j}} is either empty or it consists of a vertex or an entire edge or an entire face of TiT_{i} and (ii) there exists a constant C>0C>0 such that hTCρTh_{T}\leq C\rho_{T}.

Now we develop a strategy to generate a family of nested regular tetrahedral meshes 𝒯n\mathcal{T}_{n}, n=1,2,n=1,2,\cdots, such that 𝒯n+1\mathcal{T}_{n+1} is a refinement of 𝒯n\mathcal{T}_{n}. To this end, we introduce several notations for the given regular tetrahedral mesh 𝒯n\mathcal{T}_{n}. The mesh size of 𝒯n\mathcal{T}_{n} is defined as hn:=maxT𝒯nhTh_{n}:=\max_{T\in\mathcal{T}_{n}}h_{T}. The collection of all the faces, edges, and vertices over 𝒯n\mathcal{T}_{n} is denoted as n\mathcal{F}_{n}, n\mathcal{E}_{n} and 𝒱n\mathcal{V}_{n}, respectively. For any T𝒯nT\in\mathcal{T}_{n}, we define its faces as T:=Tn\mathcal{F}_{T}:=\partial T\bigcap\mathcal{F}_{n}, and the jjth face is denoted by FTjF_{T}^{j} for j=1,2,3,4j=1,2,3,4. Similarly, we define its edges and vertices as T:=Tn\mathcal{E}_{T}:=\partial T\bigcap\mathcal{E}_{n} and 𝒱T:=T𝒱n\mathcal{V}_{T}:=\partial T\bigcap\mathcal{V}_{n}, and the jjth edge and vertex are denoted by ETjE_{T}^{j} for j=1,,6j=1,\cdots,6, and VTjV_{T}^{j} for j=1,2,3,4j=1,2,3,4. For any FnF\in\mathcal{F}_{n}, we define the collection of elements having FF as a face as 𝒯F:={T𝒯n:FT}\mathcal{T}_{F}:=\bigcup\{T\in\mathcal{T}_{n}:F\in\mathcal{F}_{T}\} and the collection of elements having EE as an edge as 𝒯E:={T𝒯n:ET}\mathcal{T}_{E}:=\bigcup\{T\in\mathcal{T}_{n}:E\in\mathcal{E}_{T}\}. Finally, we define Pm(T)P_{m}(T) as the space of polynomial functions with total degrees at most m+m\in\mathbb{N}^{+} on element TT, and Pm(F)P_{m}(F), Pm(E)P_{m}(E) as the space of polynomial functions of degrees at most m+m\in\mathbb{N}^{+} on each face FnF\in\mathcal{F}_{n} and each edge EnE\in\mathcal{E}_{n}, respectively.

Theorem 2.1 implies that each λn(𝐤)\lambda_{n}(\mathbf{k}) is a piecewise analytic function of the wave vector 𝐤\mathbf{k}, with singular points at the branch points. Our mesh design strategy aims to identify elements within 𝒯n\mathcal{T}_{n} where λ\lambda_{\ell} and λ+1\lambda_{\ell+1} are not smooth. To accomplish this, we introduce an element-wise indicator

η(T):=minmax{1,1}q+1𝐤𝒱T{|λqλq+1|}for T𝒯n,\eta(T):=\min_{\begin{subarray}{c}\max\{1,\ell-1\}\leq q\leq\ell+1\\ \mathbf{k}\in\mathcal{V}_{T}\end{subarray}}\left\{|\lambda_{q}-\lambda_{q+1}|\right\}\quad\text{for }T\in\mathcal{T}_{n}, (3.1)

which uses the adjacent values on the vertices of each tetrahedron element to ascertain the regularity of the eigenvalues as functions of 𝐤\mathbf{k} within each element. Furthermore, the element-wise tolerance is

tol1(T)=κhTCmax,(T),\operatorname{tol}_{1}(T)=\kappa h_{T}C_{\max,\ell}(T), (3.2)

where κ23\kappa\geq 2\sqrt{3} is a constant and

Cmax,(T):=supmax{1,1}q+1𝐤T\𝐒|λq(𝐤)|\displaystyle C_{\max,\ell}(T):=\sup_{\begin{subarray}{c}\max\{1,\ell-1\}\leq q\leq\ell+1\\ \mathbf{k}\in T\backslash\mathbf{S}\end{subarray}}\left|\nabla\lambda_{q}(\mathbf{k})\right|

is an upper bound of the gradients of λq(𝐤)\lambda_{q}(\mathbf{k}) with max{1,1}q+1\max\{1,\ell-1\}\leq q\leq\ell+1.

Remark 3.1.

In the numerical experiments, we take κ=22\kappa=2\sqrt{2}, which represents a more relaxed tolerance. It reduces the computational cost, while ensuring the expected convergence rate and controlling the maximum error within n\mathcal{M}_{n}.

Give a mesh 𝒯n\mathcal{T}_{n}, the collection of marked elements is given by

n:={T𝒯n:η(T)tol1(T)}.\displaystyle\mathcal{M}_{n}:=\cup\left\{T\in\mathcal{T}_{n}:\eta(T)\leq\operatorname{tol}_{1}(T)\right\}. (3.3)

Then we apply the iterated longest edge bisection algorithm (LEB) [42] to divide each marked element TnT\in\mathcal{M}_{n} into two children elements, which typically follows two steps. First, creating a new vertex at the midpoint of its longest edge and then dividing this tetrahedron by the plane that passes through this new vertex and the two vertices opposite the longest edge. Second, performing additional bisection to avoid any hanging nodes by means of recursive algorithm, which was proven to be finite [42].

We summarize the proposed mesh refinement procedure in Algorithm 1, where the parameters tol2\operatorname{tol}_{2} and nmax1n_{\max}^{1} denote the smallest allowed element size and the maximum number of iterations, respectively.

Algorithm 1 Adaptive mesh refinement
initial mesh 𝒯1\mathcal{T}_{1}, tolerance tol2\operatorname{tol}_{2}, and maximum loop number nmax1n_{\max}^{1}
𝒯n\mathcal{T}_{n}
n1n\leftarrow 1
while nnmax1n\leq n_{\max}^{1} do
     Set n=\mathcal{M}_{n}=\emptyset
     for T𝒯nT\in\mathcal{T}_{n} do
         if η(T)tol1(T)\eta(T)\leq\operatorname{tol}_{1}(T) and hTtol2h_{T}\geq\operatorname{tol}_{2} then
              n=n{T}\mathcal{M}_{n}=\mathcal{M}_{n}\bigcup\{T\}
         end if
     end forRefine the elements in n\mathcal{M}_{n} by iterated LEB algorithm and adjust the mesh until there are no hanging nodes. nn+1n\leftarrow n+1
end while
niter:=n1n_{\operatorname{iter}}:=n-1

Next, we show that all elements containing singular points are marked for refinement. For the mesh 𝒯n\mathcal{T}_{n} at the nnth iteration, let

𝒯nR:={T𝒯n:T𝐒=} and 𝒯nS:=𝒯n\𝒯nR\displaystyle\mathcal{T}_{n}^{R}:=\cup\left\{T\in\mathcal{T}_{n}:T\cap\mathbf{S}=\emptyset\right\}\text{ and }\mathcal{T}_{n}^{S}:=\mathcal{T}_{n}\backslash\mathcal{T}_{n}^{R}

be a partition of the mesh 𝒯n\mathcal{T}_{n} based upon the singular point set 𝐒\mathbf{S}.

Theorem 3.1 (𝒯nSn\mathcal{T}_{n}^{S}\subset\mathcal{M}_{n}).

Algorithm 1 with the element-wise quantity (3.1) and the tolerance (3.2) guarantees that all elements containing branch points are marked for refinement.

Proof.

The proof follows directly from that of [50, Theorem 3.2]. ∎

In practice, we use a practical tolerance given by

C^max,(T):=maxmax{1,1}q+1𝐤𝒱T{|λq(𝐤)|},\displaystyle\hat{C}_{\max,\ell}(T):=\max_{\begin{subarray}{c}\max\{1,\ell-1\}\leq q\leq\ell+1\\ \mathbf{k}\in\mathcal{V}_{T}\end{subarray}}\left\{\left|\nabla\lambda_{q}(\mathbf{k})\right|\right\}, (3.4)

as a substitute for Cmax,(T)C_{\max,\ell}(T) which only requires jλq\partial_{j}\lambda_{q} for i=1,2,3i=1,2,3 and max{1,1}q+1\max\{1,\ell-1\}\leq q\leq\ell+1 over 𝒱T\mathcal{V}_{T}, which can be obtained from (2.11).

3.2 Element-wise interpolation over the adaptive mesh

Let {𝒯n}n=1niter\{\mathcal{T}_{n}\}_{n=1}^{n_{\operatorname{iter}}} be a sequence of nested regular tetrahedral meshes generated by Algorithm 1. First, we establish a finite element space of continuous element-wise polynomial functions VnV_{n} as the ansatz space to approximate the target band functions. Then, we define element-wise interpolation operator Π𝒯n:C()Vn\Pi_{\mathcal{T}_{n}}:C(\mathcal{B})\to V_{n} to reconstruct the target band functions on conforming finite element spaces. First, we introduce the concept of a layer of each element T𝒯nT\in\mathcal{T}_{n}.

Definition 3.2.

The layer T\ell_{T} of an element T𝒯nT\in\mathcal{T}_{n} is T:=nr\ell_{T}:=n-r, where 0rn10\leq r\leq n-1 is the number of refinements that have been performed on the element TT in Algorithm 1.

Next, we define the total degree for each element, face and edge.

Definition 3.3 (Element-wise, face-wise and edge-wise spaces of polynomial functions).

For each element T𝒯nT\in\mathcal{T}_{n}, we take PnT(T)P_{n_{T}}(T) as the local interpolation space, with

nT:={max{2,μT} if Tn2 otherwise.n_{T}:=\left\{\begin{aligned} &\max\left\{2,\left\lceil\mu{\ell}_{T}\right\rceil\right\}&&\text{ if }T\notin\mathcal{M}_{n}\\ &2&&\text{ otherwise}.\end{aligned}\right.

Here, \left\lceil\cdot\right\rceil denotes the ceiling function, the slope parameter μ>0\mu>0 will be determined by (4.11) and n\mathcal{M}_{n} is the collection of marked elements (3.3). Similarly, for each face FnF\in\mathcal{F}_{n}, let PmF(F)P_{m_{F}}(F) be the local interpolation space over FF with mF:=minT𝒯nFT{nT}m_{F}:=\min_{T\in\mathcal{T}_{n}\atop F\in\partial T}\{n_{T}\}, and let PmE(E)P_{m_{E}}(E) be the local interpolation space over EE for EnE\in\mathcal{E}_{n} with mE:=minFnEF{mF}m_{E}:=\min_{F\in\mathcal{F}_{n}\atop E\in\partial F}\{m_{F}\}.

This construction implies

maxFT{mF}nTandmaxET{mE}minFTEF{mF},T𝒯n.\max_{F\in\mathcal{F}_{T}}\{m_{F}\}\leq n_{T}\quad\mbox{and}\quad\max_{E\in\mathcal{E}_{T}}\{m_{E}\}\leq\min_{F\in\mathcal{F}_{T}\atop E\in\partial F}\{m_{F}\},\quad\forall T\in\mathcal{T}_{n}.

Finally, we define the conforming element-wise polynomial space. On a given mesh 𝒯n\mathcal{T}_{n} with {nT}T𝒯n\{n_{T}\}_{T\in\mathcal{T}_{n}}, {mE}En\{m_{E}\}_{E\in\mathcal{E}_{n}}, and {mF}Fn\{m_{F}\}_{F\in\mathcal{F}_{n}} being the element-wise, face-wise, and edge-wise degrees, the global approximation space VnV_{n} is given by

Vn:={vC():v|TPnT(T), v|FPmF(F), v|EPmE(E),T𝒯n,Fn,En},\displaystyle V_{n}:=\left\{v\in C(\mathcal{B}):v|_{T}\in P_{n_{T}}(T),\text{ }v|_{F}\in P_{m_{F}}(F),\text{ }v|_{E}\in P_{m_{E}}(E),\,\forall T\in\mathcal{T}_{n},F\in\mathcal{F}_{n},E\in\mathcal{E}_{n}\right\}, (3.5)

which is a linear space consisting of continuous piecewise polynomial functions on 𝒯n\mathcal{T}_{n} with predetermined degrees given by the triple {nT,mF,mE}\{n_{T},m_{F},m_{E}\}.

Now, we introduce the local approximation space on the reference triangular pyramid T^\hat{T}:

T^:={(x,y,z):0x1,0y1,0z1,01xyz1}.\hat{T}:=\{(x,y,z):0\leq x\leq 1,0\leq y\leq 1,0\leq z\leq 1,0\leq 1-x-y-z\leq 1\}.

The vertices of T^\hat{T} are 𝐳^1=(1,0,0)\mathbf{\hat{z}}_{1}=(1,0,0), 𝐳^2=(0,1,0)\mathbf{\hat{z}}_{2}=(0,1,0), 𝐳^3=(0,0,1)\mathbf{\hat{z}}_{3}=(0,0,1) and 𝐳^4=(0,0,0)\mathbf{\hat{z}}_{4}=(0,0,0). Let F^i\hat{F}^{i} be the face opposite to 𝐳^i\mathbf{\hat{z}}_{i} and let E^jk\hat{E}^{jk} with j>kj>k be the edge with endpoints 𝐳^j\mathbf{\hat{z}}_{j} and 𝐳^k\mathbf{\hat{z}}_{k}. For any {n,pi,qjk}(+)11\{n,p_{i},q_{jk}\}\in(\mathbb{N}^{+})^{11} with npin\geq p_{i} and piqjkp_{i}\geq q_{jk} for i,j,k=1,2,3,4i,j,k=1,2,3,4, j,kij,k\neq i, j>kj>k, (3.5) implies that the local space of polynomial functions over T^\hat{T} is

P^:={vPn(T^):v|F^iPpi(F^i) and v|E^jkPqjk(E^jk), for i,j,k=1,2,3,4, with j>k}.\displaystyle\hat{P}:=\left\{v\in P_{n}(\hat{T}):v|_{\hat{F}^{i}}\in P_{p_{i}}(\hat{F}^{i})\text{ and }v|_{\hat{E}^{jk}}\in P_{q_{jk}}(\hat{E}^{jk}),\text{ for }i,j,k=1,2,3,4,\text{ with }j>k\right\}. (3.6)

Note that P^Pn(T^)\hat{P}\subset P_{n}(\hat{T}) and P^Pn(T^)\hat{P}\subsetneq P_{n}(\hat{T}) if pi<np_{i}<n or qjk<piq_{jk}<p_{i} for certain j,kij,k\neq i with j>kj>k. Note also that the dimension of Pn(T^)P_{n}(\hat{T}) is dim(Pn(T^))=(n+33)\dim(P_{n}(\hat{T}))=(\begin{subarray}{c}n+3\\ 3\end{subarray}) and the dimension of P^\hat{P} is

dim(P^)=4+j=24k=1j1(qjk1)+i=14(pi12)+(n13).\dim(\hat{P})=4+\sum_{j=2}^{4}\sum_{k=1}^{j-1}(q_{jk}-1)+\sum_{i=1}^{4}\left(\begin{subarray}{c}p_{i}-1\\ 2\end{subarray}\right)+\left(\begin{subarray}{c}n-1\\ 3\end{subarray}\right).

Next we construct a set of basis functions for the local polynomial space P^\hat{P}. We first partition the degrees of freedom into edge, face, and internal ones according to whether they vanish over the edge, face, and boundary. The basis functions can further be classified into four types: nodal, edge, face, and internal ones, following the standard practice in the community of hphp-adaptive FEMs [1]. It enables defining conforming finite element spaces with nonuniform degrees of polynomials over the mesh.

First, the nodal shape functions are defined as the following four polynomials:

N11\displaystyle N^{1}_{1} =x,N21=y,N31=z,N41=1xyz.\displaystyle=x,\quad N^{1}_{2}=y,\quad N^{1}_{3}=z,\quad N^{1}_{4}=1-x-y-z. (3.7)

Note that for i=1,2,3,4i=1,2,3,4, we have Ni1(𝐳^i)=1N^{1}_{i}(\mathbf{\hat{z}}_{i})=1 and Ni1(𝐳)=0N^{1}_{i}(\mathbf{z})=0 for 𝐳F^i\mathbf{z}\in\hat{F}^{i}.

Second, the set of edge shape functions over each edge E^jk\hat{E}^{jk} for j=1,2,3,4j=1,2,3,4, j>kj>k are

P¯qjk(E^jk):=span{Njkqjk,ν:=Nj1Nk1Φν2(Nk1Nj1):ν=2,,qjk},\displaystyle\overline{P}_{q_{jk}}(\hat{E}^{jk}):=\text{span}\{N^{q_{jk},\nu}_{jk}:=N^{1}_{j}N^{1}_{k}\Phi_{\nu-2}(N^{1}_{k}-N^{1}_{j}):\nu=2,\cdots,q_{jk}\}, (3.8)

where Φν2(x):=xν2\Phi_{\nu-2}(x):=x^{\nu-2} is a polynomial of degree ν2\nu-2. The edge functions vanish on all vertices and all faces that do not include the edge.

Third, the face shape functions over each face F^i\hat{F}^{i} for i=1,2,3,4i=1,2,3,4 are defined as

P¯pi(F^i):={bF^i}Ppi3(F^i) for pi3.\overline{P}_{p_{i}}(\hat{F}^{i}):=\left\{b_{\hat{F}^{i}}\right\}\otimes P_{p_{i}-3}(\hat{F}^{i})\text{ for }p_{i}\geq 3. (3.9)

Here bF^i:=Nj1Nk1Nl1b_{\hat{F}^{i}}:=N^{1}_{j}N^{1}_{k}N^{1}_{l} with jklij\neq k\neq l\neq i denotes the face bubble function and Ppi3(F^i)P_{p_{i}-3}(\hat{F}^{i}) is the space of polynomials defined on F^i\hat{F}^{i} with total degree no greater than pi3p_{i}-3. Moreover, we set P0(F^i)={1}P_{0}(\hat{F}^{i})=\{1\}. Clearly, supp(bF^i)=F^i\operatorname{supp}(b_{\hat{F}^{i}})=\hat{F}^{i}.

Altogether, the set of external shape functions is given by

PE(T^):=span{Ni1:i=1,2,3,4}+j,k=1,,4j>kP¯qjk(E^jk)+i=14Ppi(F^i).P^{\mathrm{E}}(\partial\hat{T}):=\text{span}\left\{N^{1}_{i}:i=1,2,3,4\right\}+\sum_{\begin{subarray}{c}j,k=1,\cdots,4\\ j>k\end{subarray}}\overline{P}_{q_{jk}}(\hat{E}^{jk})+\sum_{i=1}^{4}P_{p_{i}}(\hat{F}^{i}).

Let bT^:=N11N21N31N41b_{\hat{T}}:=N^{1}_{1}N^{1}_{2}N^{1}_{3}N^{1}_{4} be the basic bubble function on the reference element T^\hat{T} that vanishes over T^\partial\hat{T}. Then the set of internal shape functions PnI(T^)P^{I}_{n}(\hat{T}) are defined as

PnI(T^):={bT^}Pn4(T^) for n4 with P0(T^)={1}.P^{\mathrm{I}}_{n}(\hat{T}):=\left\{b_{\hat{T}}\right\}\otimes P_{n-4}(\hat{T})\text{ for }n\geq 4\text{ with }P_{0}(\hat{T})=\{1\}. (3.10)

Next, we introduce a set of Lagrangian interpolation points over the reference triangular pyramid T^\hat{T} with the local polynomial space P^\hat{P} (3.6) such that its restriction to each face and each edge serves as the Lagrangian interpolation points within the face or edge called the Lobatto tetrahedral (LTT) set [37]. First we introduce a 1D grid in the interval [0,1][0,1] comprising a set of n+1n+1 sampling points vi=12(1+ti)v_{i}=\tfrac{1}{2}(1+t_{i}), for i=2,3,,ni=2,3,\cdots,n, where tit_{i} are the zeros of the (n1)(n-1)th degree Lobatto polynomial. The endpoints are v1=0v_{1}=0 and vn+1=1v_{n+1}=1.

Definition 3.4 (Sampling points over each edge, face and internal).

Suppose the edge degrees on E^jk\hat{E}^{jk}, face degrees on F^i\hat{F}^{i} and total degree are qjkq_{jk}, pip_{i} and nn for i,j,k=1,,4i,j,k=1,\cdots,4, j>kj>k. Then the sampling points are defined below.

  • The set of sampling points on all edges are

    𝒮={Mjkvν:j,k=1,2,3,4,j>k and ν=1,qjk+1},\mathcal{S}=\{M_{jk}v_{\nu}:j,k=1,2,3,4,j>k\text{ and }\nu=1\cdots,q_{jk}+1\}, (3.11)

    with MjkM_{jk} being the linear transformation maps [0,1][0,1] to E^jk\hat{E}^{jk}.

  • The sampling points on F^1,,F^4\hat{F}^{1},\cdots,\hat{F}^{4} are (0,ζ,η)(0,\zeta,\eta), (ζ,0,η)(\zeta,0,\eta), (ζ,η,0)(\zeta,\eta,0), (ζ,η,ξ)(\zeta,\eta,\xi) with

    ζ:=13(1+2vjvkv),η:=13(1vj+2vkv),ξ:=1ζη,\zeta:=\tfrac{1}{3}(1+2v_{j}-v_{k}-v_{\ell}),\quad\eta:=\tfrac{1}{3}(1-v_{j}+2v_{k}-v_{\ell}),\quad\xi:=1-\zeta-\eta, (3.12)

    for j=1,2,,pmj=1,2,\cdots,{p}_{m}, k=2,3,,pm+2jk=2,3,\cdots,{p}_{m}+2-j and =pm+3jk\ell={p}_{m}+3-j-k satisfying ζ,η0\zeta,\eta\neq 0, ζ+η1\zeta+\eta\neq 1 for F^m\hat{F}^{m}, m=1,2,3m=1,2,3 and ζ,η,ξ0\zeta,\eta,\xi\neq 0, ξ+ζ1\xi+\zeta\neq 1, ζ+η1\zeta+\eta\neq 1, ξ+η1\xi+\eta\neq 1, ξ+ζ1\xi+\zeta\neq 1 for F^4\hat{F}^{4}. Here, pm{p}_{m} is the face-wise degree on the given face F^m\hat{F}^{m}.

  • The internal sampling points are introduced at the positions (ξ,ζ,η)(\xi,\zeta,\eta) with

    ξ=14(1+3vivjvkv),ζ=14(1vi+3vjvkv),η=14(1vivj+3vkv),\xi=\tfrac{1}{4}(1+3v_{i}-v_{j}-v_{k}-v_{\ell}),\,\,\zeta=\tfrac{1}{4}(1-v_{i}+3v_{j}-v_{k}-v_{\ell}),\,\,\eta=\tfrac{1}{4}(1-v_{i}-v_{j}+3v_{k}-v_{\ell}), (3.13)

    for i=2,3,,ni=2,3,\cdots,n, j=2,3,,n+1ij=2,3,\cdots,n+1-i, k=2,3,,n+2ijk=2,3,\cdots,n+2-i-j, and =n+4ijk\ell=n+4-i-j-k.

Next, we give an interpolation operator Π:C(T^)P^\Pi:C(\hat{T})\to\hat{P}, such that for all fC(T^)f\in C(\hat{T}), there holds

{Πf|F^iPpi(F^i) for i=1,2,3,4,Πf|E^jkPqjk(E^jk) for j,k=1,2,3,4, and j>k,ΠfP^.\left\{\begin{aligned} \Pi f\big{|}_{\hat{F}^{i}}&\in P_{p_{i}}(\hat{F}^{i})\quad\qquad\text{ for }i=1,2,3,4,&\\ \Pi f\big{|}_{\hat{E}^{jk}}&\in P_{q_{jk}}(\hat{E}^{jk})\qquad\text{ for }j,k=1,2,3,4,\text{ and }j>k,&\\ \Pi f&\in\hat{P}.&\end{aligned}\right. (3.14)

First, we introduce the external interpolation E:C(T^)PE(T^)\operatorname{E}:C(\partial\hat{T})\to P^{E}(\partial\hat{T}). For any function fC(T^)f\in C(\partial\hat{T}), the external interpolation Ef\operatorname{E}f is a linear interpolation on a set of sampling points on T^\partial\hat{T} given by Definition 3.4 in the set of external shape functions PE(T^)P^{\mathrm{E}}(\partial\hat{T}). Next, we extend E\operatorname{E} from T^\partial\hat{T} to T^\hat{T} and denote it as 𝐄:PE(T^)P^\mathbf{E}:P^{\mathrm{E}}(\partial\hat{T})\to\hat{P}. Then we define the internal interpolation operator I:C(T^)PnI(T^)\operatorname{I}:C(\hat{T})\to P^{\mathrm{I}}_{n}(\hat{T}) as the Lagrange interpolation on internal sampling point set (3.13) using the set of internal shape functions (3.10). Denote the Lebesgue constant of I\operatorname{I} as Υn\Upsilon_{n}, i.e.,

If,T^Υnf,T^,fC(T^).\displaystyle\|\operatorname{I}f\|_{\infty,\hat{T}}\leq\Upsilon_{n}\|f\|_{\infty,\hat{T}},\quad\forall f\in C(\hat{T}). (3.15)

It has been shown numerically [37, 7] that Υn\Upsilon_{n} has moderate growth with respect to the total degree nn. Finally, we can define the element-wise interpolation Π:C(T^)P^\Pi:C(\hat{T})\to\hat{P} by

Πf:=𝐄(Ef)+I(f𝐄(Ef)),fC(T^).\Pi f:=\mathbf{E}(\operatorname{E}f)+\operatorname{I}(f-\mathbf{E}(\operatorname{E}f)),\quad\forall f\in C(\hat{T}). (3.16)

By construction, Πf\Pi f fulfills the desired properties (3.14). Moreover, the following properties hold. (i) Πp=p\Pi p=p, for all pP^p\in\hat{P}; (ii) Πf|E^jkPqjk(E^jk)\Pi f\big{|}_{\hat{E}^{jk}}\in P_{q_{jk}}(\hat{E}^{jk}) is the Lagrange interpolation on the sampling point set (3.11) along each edge E^jk\hat{E}^{jk} for j,k=1,2,3,4j,k=1,2,3,4 with j>kj>k; (iii) Πf|F^iPpi(F^i)\Pi f\big{|}_{\hat{F}^{i}}\in P_{p_{i}}(\hat{F}^{i}) is the Lagrange interpolation on the sampling point set (3.12) along each face F^i\hat{F}^{i} for i=1,2,3,4i=1,2,3,4; (iv) Πf|T^\T=IfPnI(T^)\Pi f\big{|}_{\hat{T}^{\backslash}\partial T}=\operatorname{I}f\in P^{I}_{n}(\hat{T}) is the Lagrange interpolation of ff on the sampling point set inside T^\hat{T} (3.13).

For a given mesh 𝒯n\mathcal{T}_{n}, we define the global approximation operator Π𝒯n:C()Vn\Pi_{\mathcal{T}_{n}}:C(\mathcal{B})\to V_{n} by applying Π\Pi to each element T𝒯nT\in\mathcal{T}_{n} as

Π𝒯nf|T:=(Π(fMT))MT1,T𝒯n,\Pi_{\mathcal{T}_{n}}f\big{|}_{T}:=\left(\Pi(f\circ M_{T})\right)\circ M_{T}^{-1},\quad\forall T\in\mathcal{T}_{n}, (3.17)

where the affine transformation MT:=AT𝐳+𝐛M_{T}:=A_{T}\mathbf{z}+\mathbf{b} (AT3×3A_{T}\in\mathbb{R}^{3\times 3} is invertible and 𝐛3\mathbf{b}\in\mathbb{R}^{3}) maps the reference tetrahedron T^\hat{T} to the ”physical” element T¯\bar{T}.

Now we can give the following algorithm for approximating λ(𝐤)\lambda_{\ell}(\mathbf{k}) and λ+1(𝐤)\lambda_{\ell+1}(\mathbf{k}). By sequentially generating the adaptive mesh and the polynomial degree required for each element, each face, and each edge, we can then use the interpolation formula (3.17) to adaptively interpolate λq\lambda_{q} for q=n,n+1q=n,n+1.

Algorithm 2 hphp-adaptive sampling algorithm
𝒯niter\mathcal{T}_{n_{\operatorname{iter}}}: the outcome of adaptive mesh from Algorithm 1; {T}T𝒯niter\{\ell_{T}\}_{T\in\mathcal{T}_{n_{\operatorname{iter}}}}: layer of each element defined in Definition 3.2; μ\mu: a positive parameter defined in Definition 3.3 satisfying (4.11);
λq\lambda_{q}, q=,+1q=\ell,\ell+1 Define element-wise, face-wise and edge-wise degrees: {nT}T𝒯niter\{n_{T}\}_{T\in\mathcal{T}_{n_{\operatorname{iter}}}}, {mF}Fniter\{m_{F}\}_{F\in\mathcal{F}_{n_{\operatorname{iter}}}} and {mE}Eniter\{m_{E}\}_{E\in\mathcal{E}_{n_{\operatorname{iter}}}} by Definition 3.3; Compute the local interpolation Πλq\Pi\lambda_{q} by (3.16); Compute the global interpolation Π𝒯niterλq\Pi_{\mathcal{T}_{n_{\operatorname{iter}}}}\lambda_{q} by (3.17).

4 Convergence analysis

This section is devoted to the convergence analysis of Algorithm 2. We prove an exponential convergence rate in Theorem 4.5 when the number of crossings among {λq:max{1,1}q+2}\{\lambda_{q}:\max\{1,\ell-1\}\leq q\leq\ell+2\} is finite, and an algebraic convergence in Theorem 4.6 otherwise. To this end, we first establish the approximation property of the local polynomial interpolation operator Π\Pi in Theorem 4.3. Then we formulate the element-wise approximation property of Π𝒯n\Pi_{\mathcal{T}_{n}} in Theorems 4.5 and 4.6 under assumption (4.11) on the slope parameter μ\mu such that the approximation error in the marked element patch n\mathcal{M}_{n} dominates.

4.1 Local approximation error

First we establish the approximation property of the local interpolation operator Π\Pi. Recall that for any element-wise, face-wise, and edge-wise degrees {nT}T𝒯n\{n_{T}\}_{T\in\mathcal{T}_{n}}, {mF}Fn\{m_{F}\}_{F\in\mathcal{F}_{n}}, and {mE}En\{m_{E}\}_{E\in\mathcal{E}_{n}}, the local space P^\hat{P} is defined by (3.6). Below the notation ABA\lesssim B stands for that A/BA/B is bounded above by a constant independent of the total degree, face degree, and edge degree.

First, we establish the stability of the external interpolation En:C(T^)PE(T^)E_{n}:C(\partial\hat{T})\to P^{E}(\partial\hat{T}). Although there is no proof, numerical results show that the Lebesgue constant of the set of sampling points on each face F^i\hat{F}^{i} (3.12) is 𝒪(pi)\mathcal{O}(p_{i}) [3]. Hence we make the following assumption

Ef,F^i\F^ipif,F^i\F^i,fC(F^i\F^i).\displaystyle\|\operatorname{E}f\|_{\infty,\hat{F}^{i}\backslash\partial\hat{F}^{i}}\lesssim p_{i}\|f\|_{\infty,\hat{F}^{i}\backslash\partial\hat{F}^{i}},\quad\forall f\in C(\hat{F}^{i}\backslash\partial\hat{F}^{i}). (4.1)
Theorem 4.1.

Let (4.1) hold. Then the Lagrange interpolation E|F^i\operatorname{E}|_{\hat{F}^{i}} along each face F^i\hat{F}^{i} is stable, for i=1,2,3,4i=1,2,3,4. Moreover, there holds

Ef,F^ipilogpif,F^ifC(F^i).\|\operatorname{E}f\|_{\infty,\hat{F}^{i}}\lesssim p_{i}\log p_{i}\|f\|_{\infty,\hat{F}^{i}}\quad\forall f\in C(\hat{F}^{i}). (4.2)
Proof.

This result follows from [50, Theorem 5.3]. ∎

Theorem 4.2.

For each fC(T^)f\in C(\partial\hat{T}) with f|F^iPpi(F^i)f|_{\hat{F}^{i}}\in P_{p_{i}}(\hat{F}^{i}) for all i=1,2,3,4i=1,2,3,4 and f|E^jkPqjk(E^jk)f|_{\hat{E}^{jk}}\in P_{q_{jk}}(\hat{E}^{jk}) for all j,k=1,2,3,4j,k=1,2,3,4 with j>kj>k, the extension 𝐄:PE(T^)P^\mathbf{E}:P^{\mathrm{E}}(\partial\hat{T})\to\hat{P} satisfies

𝐄(f),T^f,T^.\|\mathbf{E}(f)\|_{\infty,\hat{T}}\lesssim\|f\|_{\infty,\partial\hat{T}}. (4.3)

Moreover, the extension map 𝐄:f𝐄(f)\mathbf{E}:f\mapsto\mathbf{E}(f) is a bounded linear operator.

Proof.

First, we assume that ff vanishes at all vertices, all edges and all faces but one face. We assume this face is the third face F^3={(x,y,z):0x,y,1xy1,z=0}\hat{F}^{3}=\{(x,y,z):0\leq x,y,1-x-y\leq 1,z=0\}, i.e., only face shape functions over F^3\hat{F}^{3} remain (3.9). Let Pp33(F^3):=span{ϕν:ν=1,,μ(p3)}P_{p_{3}-3}(\hat{F}^{3}):=\text{span}\{\phi_{\nu}:\nu=1,\cdots,\mu(p_{3})\} with μ(p3):=(p312)\mu(p_{3}):=(\begin{subarray}{c}p_{3}-1\\ 2\end{subarray}), then 𝐄(f)\mathbf{E}(f) can be written in the form

𝐄(f)=bF^3ν=1μ(p3)cνϕν.\mathbf{E}(f)=b_{\hat{F}^{3}}\sum_{\nu=1}^{\mu(p_{3})}c_{\nu}{\phi}_{\nu}. (4.4)

Then there holds

max(x,y,z)T^|𝐄(f)|=max(x,y,z)T^|bF^3ν=1μ(p3)cνϕν|\displaystyle\max_{(x,y,z)\in\hat{T}}|\mathbf{E}(f)|=\max_{(x,y,z)\in\hat{T}}\bigg{|}b_{\hat{F}^{3}}\sum_{\nu=1}^{\mu(p_{3})}c_{\nu}\phi_{\nu}\bigg{|}
=\displaystyle= max0x1,0y1,0z1x+y+z1|xy(1xyz)0i,jp330i+jp33cijxiyj|.\displaystyle\max_{\begin{subarray}{c}0\leq x\leq 1,0\leq y\leq 1,0\leq z\leq 1\\ x+y+z\leq 1\end{subarray}}\bigg{|}xy(1-x-y-z)\cdot\sum_{\begin{subarray}{c}0\leq i,j\leq p_{3}-3\\ 0\leq i+j\leq p_{3}-3\end{subarray}}c_{ij}x^{i}y^{j}\bigg{|}.

Consequently, straightforward calculation leads to

max(x,y,z)T^|𝐄(f)|\displaystyle\max_{(x,y,z)\in\hat{T}}|\mathbf{E}(f)| =max0x1,0y1,z=0x+y1|xy(1xyz)0i,jp330i+jp33cijkxiyj|\displaystyle=\max_{\begin{subarray}{c}0\leq x\leq 1,0\leq y\leq 1,z=0\\ x+y\leq 1\end{subarray}}\bigg{|}xy(1-x-y-z)\cdot\sum_{\begin{subarray}{c}0\leq i,j\leq p_{3}-3\\ 0\leq i+j\leq p_{3}-3\end{subarray}}c_{ijk}x^{i}y^{j}\bigg{|}
=max(x,y,z)T^|f|.\displaystyle=\max_{(x,y,z)\in\partial\hat{T}}|f|. (4.5)

Second, we assume that ff vanishes at all faces, edges and vertices but one edge. We assume the edge to be E^21\hat{E}^{21}. Then 𝐄(p)\mathbf{E}(p) can be written in the form

𝐄(f)=N21N11ν=2q21cν(N21N11)ν2=xyν=2q21cν(yx)ν2.\displaystyle\mathbf{E}(f)=N^{1}_{2}N^{1}_{1}\sum_{\nu=2}^{q_{21}}c_{\nu}(N^{1}_{2}-N^{1}_{1})^{\nu-2}=xy\sum_{\nu=2}^{q_{21}}c_{\nu}(y-x)^{\nu-2}.

This leads to

max(x,y)T^|𝐄(f)|\displaystyle\max_{(x,y)\in\hat{T}}|\mathbf{E}(f)| =max(x,y)T^|xyν=2q21cν(yx)ν2|\displaystyle=\max_{(x,y)\in\hat{T}}\left|xy\sum_{\nu=2}^{q_{21}}c_{\nu}(y-x)^{\nu-2}\right|
=max0x1,0y1,0z1x+y+z1|xyν=2q21cν(yx)ν2|\displaystyle=\max_{\begin{subarray}{c}0\leq x\leq 1,0\leq y\leq 1,0\leq z\leq 1\\ x+y+z\leq 1\end{subarray}}\left|xy\sum_{\nu=2}^{q_{21}}c_{\nu}(y-x)^{\nu-2}\right|
=max0x1,0y1,z=0x+y=1|xyν=2q21cν(yx)ν2|=max(x,y)T^|f|.\displaystyle=\max_{\begin{subarray}{c}0\leq x\leq 1,0\leq y\leq 1,z=0\\ x+y=1\end{subarray}}\left|xy\sum_{\nu=2}^{q_{21}}c_{\nu}(y-x)^{\nu-2}\right|=\max_{(x,y)\in\partial\hat{T}}|f|. (4.6)

Third, we assume that ff vanishes at all faces, edges and vertices, but one vertex. Similarly, we have

max(x,y)T^|𝐄(f)|=max(x,y)T^|f|.\displaystyle\max_{(x,y)\in\hat{T}}|\mathbf{E}(f)|=\max_{(x,y)\in\partial\hat{T}}|f|. (4.7)

Finally, we can prove the general case of fC(T^)f\in C(\partial\hat{T}) with f|F^iPpi(F^i)f|_{\hat{F}^{i}}\in P_{p_{i}}(\hat{F}^{i}) for all i=1,2,3,4i=1,2,3,4 and f|E^jkPqjk(E^jk)f|_{\hat{E}^{jk}}\in P_{q_{jk}}(\hat{E}^{jk}) for all j,k=1,2,3,4j,k=1,2,3,4 with j>kj>k. The linearity of the extension operator 𝐄\mathbf{E}, together with (4.5), (4.6) and (4.7), implies

max(x,y,z)T^|𝐄(f)|=max(x,y,z)T^|i=14𝐄(f|F^i)+j,k=1j>k4𝐄(f|E^jk)+=14N1f(𝐳^)|\displaystyle\max_{(x,y,z)\in\hat{T}}|\mathbf{E}(f)|=\max_{(x,y,z)\in\hat{T}}\bigg{|}\sum_{i=1}^{4}\mathbf{E}(f\big{|}_{\hat{F}^{i}})+\sum_{\begin{subarray}{c}j,k=1\\ j>k\end{subarray}}^{4}\mathbf{E}(f\big{|}_{\hat{E}^{jk}})+\sum_{\ell=1}^{4}N_{\ell}^{1}f(\hat{\mathbf{z}}_{\ell})\bigg{|}
\displaystyle\leq i=14max(x,y,z)T^|𝐄(f|F^i)|+j,k=1j>k4max(x,y)T^|𝐄(f|E^jk)|+=14max(x,y)T^|N1f(𝐳^)|\displaystyle\sum_{i=1}^{4}\max_{(x,y,z)\in\hat{T}}|\mathbf{E}(f\big{|}_{\hat{F}^{i}})|+\sum_{\begin{subarray}{c}j,k=1\\ j>k\end{subarray}}^{4}\max_{(x,y)\in\hat{T}}|\mathbf{E}(f\big{|}_{\hat{E}^{jk}})|+\sum_{\ell=1}^{4}\max_{(x,y)\in\hat{T}}|N_{\ell}^{1}f(\hat{\mathbf{z}}_{\ell})|
=\displaystyle= i=14max(x,y,z)T^|𝐄(f|F^i)|+j,k=1j>k4max(x,y)T^|𝐄(f|E^jk)|+=14max(x,y)T^|N1f(𝐳^)|f,T^,\displaystyle\sum_{i=1}^{4}\max_{(x,y,z)\in\partial\hat{T}}|\mathbf{E}(f\big{|}_{\hat{F}^{i}})|+\sum_{\begin{subarray}{c}j,k=1\\ j>k\end{subarray}}^{4}\max_{(x,y)\in\partial\hat{T}}|\mathbf{E}(f\big{|}_{\hat{E}^{jk}})|+\sum_{\ell=1}^{4}\max_{(x,y)\in\partial\hat{T}}|N_{\ell}^{1}f(\hat{\mathbf{z}}_{\ell})|\lesssim\|f\|_{\infty,\partial\hat{T}},

where f|F^if\big{|}_{\hat{F}^{i}} denotes the face polynomials interpolating the sampling points over F^i\hat{F}^{i}, i=1,,4i=1,\cdots,4; f|E^jkf\big{|}_{\hat{E}^{jk}} are edge polynomials interpolating the sampling points over one edge, j,k=1,,4j,k=1,\cdots,4, j>kj>k; =14N1f(𝐳^)\sum_{\ell=1}^{4}N_{\ell}^{1}f(\hat{\mathbf{z}}_{\ell}) are nodal shape functions interpolating 𝐳^\hat{\mathbf{z}}_{\ell}, =1,,4\ell=1,\cdots,4. This proves the desired result. ∎

Finally, we can state the following quasi-optimal approximation property of Π\Pi (3.16).

Theorem 4.3 (Quasi-optimal approximation property of Π\Pi).

For every n+n\in\mathbb{N}^{+}, the interpolation operator Π:C(T^)P^\Pi:C(\hat{T})\to\hat{P} satisfies

fΠf,T^Cstab(n)infpP^fp,T^ with Cstab(n):=Υnnlogn.\|f-\Pi f\|_{\infty,\hat{T}}\lesssim{\mathrm{C}_{\mathrm{stab}}(n)}\inf_{p\in\hat{P}}\|f-p\|_{\infty,\hat{T}}\text{ with }{\mathrm{C}_{\mathrm{stab}}(n)}:=\Upsilon_{n}n\log n.\
Proof.

The stability of E\operatorname{E} and 𝐄\mathbf{E} in (4.2) and (4.3) gives the stability of 𝐄E:C(T^)P^\mathbf{E}\operatorname{E}:C(\partial\hat{T})\to\hat{P}:

𝐄(Ef),T^Ef,T^nlognf,T^.\|\mathbf{E}(\operatorname{E}f)\|_{\infty,\hat{T}}\lesssim\|\operatorname{E}f\|_{\infty,\partial\hat{T}}\lesssim n\log n\|f\|_{\infty,\partial\hat{T}}. (4.8)

Together with the stability estimate of the internal interpolation operator I\operatorname{I} in (3.15), we derive

Πf,T^\displaystyle\|\Pi f\|_{\infty,\hat{T}} 𝐄(Ef),T^+I(f𝐄(Ef)),T^\displaystyle\lesssim\|\mathbf{E}(\operatorname{E}f)\|_{\infty,\hat{T}}+\|\operatorname{I}(f-\mathbf{E}(\operatorname{E}f))\|_{\infty,\hat{T}}
nlognf,T^+Υnf𝐄(Ef),T^\displaystyle\lesssim n\log n\|f\|_{\infty,\partial\hat{T}}+\Upsilon_{n}\|f-\mathbf{E}(\operatorname{E}f)\|_{\infty,\hat{T}}
nlognf,T^+Υn(f,T^+𝐄(Ef),T^).\displaystyle\lesssim n\log n\|f\|_{\infty,\partial\hat{T}}+\Upsilon_{n}\left(\|f\|_{\infty,\hat{T}}+\|\mathbf{E}(\operatorname{E}f)\|_{\infty,\hat{T}}\right).

In view of (4.8), we obtain

Πf,T^nlognf,T^+Υnf,T^+Υnnlognf,T^Υnnlognf,T^.\|\Pi f\|_{\infty,\hat{T}}\lesssim n\log n\|f\|_{\infty,\partial\hat{T}}+\Upsilon_{n}\|f\|_{\infty,\hat{T}}+\Upsilon_{n}n\log n\|f\|_{\infty,\partial\hat{T}}\lesssim\Upsilon_{n}n\log n\|f\|_{\infty,\hat{T}}.

Note that Πϕ=ϕ\Pi\phi=\phi for all ϕP^\phi\in\hat{P}. Consequently, we arrive at

fΠϕ,T^=(fϕ)Π(fϕ),T^Υnnlognfϕ,T^,ϕP^.\|f-\Pi\phi\|_{\infty,\hat{T}}=\|(f-\phi)-\Pi(f-\phi)\|_{\infty,\hat{T}}\lesssim\Upsilon_{n}n\log n\|f-\phi\|_{\infty,\hat{T}},\quad\forall\phi\in\hat{P}.

Taking the infimum over all ϕP^\phi\in\hat{P} leads to the desired assertion. ∎

Theorem 4.4 (Element-wise approximation property of Π𝒯n\Pi_{\mathcal{T}_{n}}).

Let nn\in\mathbb{N} be sufficiently large. Let 𝒯n\mathcal{T}_{n} with element-wise, face-wise, and edge-wise degrees {nT}T𝒯n\{n_{T}\}_{T\in\mathcal{T}_{n}}, {mF}Fn\{m_{F}\}_{F\in\mathcal{F}_{n}} and {mE}En\{m_{E}\}_{E\in\mathcal{E}_{n}} be generated by Algorithm 1, Definitions 3.3 and let the conforming element-wise polynomial space be given by (3.5). Then for q=,+1q=\ell,\ell+1, there holds

λqΠ𝒯nλq,T\displaystyle\|\lambda_{q}-\Pi_{\mathcal{T}_{n}}\lambda_{q}\|_{\infty,T} CbpahT|λq|W1,(T)\displaystyle\lesssim\mathrm{C}_{\rm bpa}h_{T}|\lambda_{q}|_{W^{1,\infty}(T)} for Tn,\displaystyle\text{for }T\in\mathcal{M}_{n}, (4.9)
λqΠ𝒯nλq,T\displaystyle\|\lambda_{q}-\Pi_{\mathcal{T}_{n}}\lambda_{q}\|_{\infty,T} Cstab(nT)CbpaC(n~)(CconsthTn~)n~|λq|Wn~,(T)\displaystyle\lesssim{\mathrm{C}_{\mathrm{stab}}(n_{T})}\mathrm{C}_{\rm bpa}C(\tilde{n})\left(\frac{\mathrm{C}_{\rm const}h_{T}}{\tilde{n}}\right)^{\tilde{n}}|\lambda_{q}|_{W^{\tilde{n},\infty}(T)} for T𝒯n\n,\displaystyle\text{for }T\in\mathcal{T}_{n}\backslash\mathcal{M}_{n}, (4.10)

where Cbpa\mathrm{C}_{\rm bpa} is a bounded constant independent of qq, n~\tilde{n} and hT^h_{\hat{T}}, n~:=minET{mE}+1\tilde{n}:=\min_{E\in\mathcal{E}_{T}}\{m_{E}\}+1, Cconst:=hT^ρT^=3+32\mathrm{C}_{\rm const}:=\frac{h_{\hat{T}}}{\rho_{\hat{T}}}=\frac{3+\sqrt{3}}{\sqrt{2}}, and ||Wk,(D)|\cdot|_{W^{k,\infty}(D)} is the Wk,(D)W^{k,\infty}(D) semi-norm. Furthermore, if the slope parameter μ\mu satisfies the following condition for all T𝒯n\nT\in\mathcal{T}_{n}\backslash\mathcal{M}_{n}

ln(Cstab(μT)C(nT+1)Cλq,TμT+1)+μT+1(lnCconst+F(T)lnμT+1)ln(Cmaxq)+F(1),\ln\left({\mathrm{C}_{\mathrm{stab}}(\mu\ell_{T})}C(n_{T}+1)C_{\lambda_{q},T}^{\left\lceil\mu\ell_{T}+1\right\rceil}\right)+\left\lceil\mu\ell_{T}+1\right\rceil\left(\ln\mathrm{C}_{\rm const}+F(\ell_{T})-\ln\left\lceil\mu\ell_{T}+1\right\rceil\right)\lesssim\ln\left(C_{\max}^{q}\right)+F(1), (4.11)

then there holds

maxT𝒯n\nλqΠ𝒯nλq,TminTnλqΠ𝒯nλq,T.\displaystyle\max_{T\in\mathcal{T}_{n}\backslash\mathcal{M}_{n}}\|\lambda_{q}-\Pi_{\mathcal{T}_{n}}\lambda_{q}\|_{\infty,T}\lesssim\min_{T\in\mathcal{M}_{n}}\|\lambda_{q}-\Pi_{\mathcal{T}_{n}}\lambda_{q}\|_{\infty,T}.

Here, Cmaxq:=maxTn|λq|W1,(T)C_{\max}^{q}:=\max_{T\in\mathcal{M}_{n}}|\lambda_{q}|_{W^{1,\infty}(T)}, Cλq,TμT+1:=|λq|WμT+1,(T)C_{\lambda_{q},T}^{\left\lceil\mu\ell_{T}+1\right\rceil}:=|\lambda_{q}|_{W^{\left\lceil\mu\ell_{T}+1\right\rceil,\infty}(T)}, and F():=lnh1n3ln2F(\ell):=\ln h_{1}-\frac{n-\ell}{3}\ln 2 with h1h_{1} being the initial mesh size.

Proof.

The proof is similar to Theorem 5.4 in [50], but with hT^ρT^=3+32\frac{h_{\hat{T}}}{\rho_{\hat{T}}}=\frac{3+\sqrt{3}}{\sqrt{2}} and hTh12(nT)/3h_{T}\sim h_{1}2^{-(n-\ell_{T})/3}. ∎

4.2 Global approximation error

Now assuming that the slope parameter μ\mu in Definition 3.3 satisfies (4.11), we present the convergence rates of Algorithm 2 when the number of crossings among {λq:max{1,1}q+2}\{\lambda_{q}:\max\{1,\ell-1\}\leq q\leq\ell+2\} is finite and infinite. We denote NN to be the number of sampling points in Algorithm 2. The proofs to Theorems 4.5 and 4.6 follow in a similar manner to that of [50, Theorems 5.5 and 5.6].

Theorem 4.5 (Convergence rate for Algorithm 2 with finite branch points).

Let nitern_{\operatorname{iter}}\in\mathbb{N} be sufficiently large. Let 𝒯niter\mathcal{T}_{n_{\operatorname{iter}}} with element-wise, face-wise, and edge-wise degrees {nT}T𝒯n\{n_{T}\}_{T\in\mathcal{T}_{n}}, {mF}Fn\{m_{F}\}_{F\in\mathcal{F}_{n}}, and {mE}En\{m_{E}\}_{E\in\mathcal{E}_{n}} be generated by Algorithm 1, Definition 3.3, the conforming element-wise polynomial space be defined in (3.5). If the slope parameter μ\mu satisfies (4.11) and the number of crossing among {λq:max{1,1}q+2}\{\lambda_{q}:\max\{1,\ell-1\}\leq q\leq\ell+2\} is finite, then there holds

λqΠ𝒯niterλq,exp(bN14)|λ|W1,() for q= and +1.\|\lambda_{q}-\Pi_{\mathcal{T}_{n_{\operatorname{iter}}}}\lambda_{q}\|_{\infty,\mathcal{B}}\lesssim\exp(-bN^{\frac{1}{4}})|\lambda|_{W^{1,\infty}(\mathcal{B})}\qquad\text{ for }q=\ell\text{ and }\ell+1.

Here, the constant b:=13ln2b:=\frac{1}{3}\ln 2.

Proof.

Note that when the number of iterations nitern_{\operatorname{iter}} is sufficiently large,

hTh12niter/3 for all Tniter, and Nniter4\displaystyle h_{T}\sim h_{1}2^{-n_{\operatorname{iter}}/3}\text{ for all }T\in\mathcal{M}_{n_{\operatorname{iter}}},\text{ and }N\sim{n_{\operatorname{iter}}}^{4} (4.12)

if the number of branch points is finite. Combining with (4.11) and (4.9), we derive

λqΠ𝒯niterλq,\displaystyle\|\lambda_{q}-\Pi_{\mathcal{T}_{n_{\operatorname{iter}}}}\lambda_{q}\|_{\infty,\mathcal{B}} minTiterλqΠ𝒯iterλq,T\displaystyle\lesssim\min_{T\in\mathcal{M}_{\operatorname{iter}}}\|\lambda_{q}\Pi_{\mathcal{T}_{\operatorname{iter}}}\lambda_{q}\|_{\infty,T}
CbpahT|λq|W1,(T)\displaystyle\lesssim\mathrm{C}_{\rm bpa}h_{T}|\lambda_{q}|_{W^{1,\infty}(T)}
Cbpah1(21/3)N14|λq|W1,(T).\displaystyle\lesssim\mathrm{C}_{\rm bpa}h_{1}(2^{1/3})^{-N^{\frac{1}{4}}}|\lambda_{q}|_{W^{1,\infty}(T)}.

Here, we have used (4.12) in the last step, and this proves the desired assertion. ∎

Theorem 4.6 (Convergence rate for Algorithm 2 with infinite branch points).

Under the conditions of Theorem 4.5, if the number of crossing among {λq:max{1,1}q+2}\{\lambda_{q}:\max\{1,\ell-1\}\leq q\leq\ell+2\} is infinite, then

λqΠ𝒯niterλq,N1|λj|W1,() for q= and +1.\|\lambda_{q}-\Pi_{\mathcal{T}_{n_{\operatorname{iter}}}}\lambda_{q}\|_{\infty,\mathcal{B}}\lesssim N^{-1}|\lambda_{j}|_{W^{1,\infty}(\mathcal{B})}\qquad\text{ for }q=\ell\text{ and }\ell+1.
Proof.

Note that when the number of iterations nitern_{\operatorname{iter}} is sufficiently large,

hTh12niter/3 for all Tniter, and N2niter\displaystyle h_{T}\sim h_{1}2^{-n_{\operatorname{iter}}/3}\text{ for all }T\in\mathcal{M}_{n_{\operatorname{iter}}},\text{ and }N\sim 2^{n_{\operatorname{iter}}}

if the number of branch points is infinite. Then a similar argument as in the proof to Theorem 4.5 leads to the desired assertion. ∎

Remark 4.1.

When uniform refinement and fixed low-degree element-wise interpolation are utilized, the number of elements is approximately 2niter\sim 2^{n_{\operatorname{iter}}}, which indicates a convergence rate of 𝒪(N1/3)\mathcal{O}(N^{-1/3}). Furthermore, the decay rate remains only 𝒪(N1/3)\mathcal{O}(N^{-1/3}) for the global polynomial interpolation method proposed in [51]. Hence, Algorithm 2 achieves at least triple convergence rate compared with the uniform refinement with the fixed low-degree interpolation (degree=2 in our numerical experiment), as well as the global polynomial interpolation method.

5 Numerical experiments and discussions

Now we apply Algorithm 2 to the design of 3D PhCs with wide band gaps. We take two cubic unit cells Ω=[0,a]3\Omega=[0,a]^{3} with lattice constant a+a\in\mathbb{R}^{+} filled with air embedded with six silicon blocks and a central silicon sphere. The length, width and height of the blocks, and radius of the sphere are design variables. Although the assumption of a symmetric unit cell facilitates computation, a symmetry reduction of the unit cell can open wider band gaps or create new band gaps [20, 16, 17]. Thus, adding more design variables to describe an asymmetrical unit cell has great potential to maximize photonic band gaps. Algorithm 2 allows greatly reducing the associated high computational complexity. We utilize [9] to implement the iterative LEB algorithm involved in Algorithm 1.

5.1 Experimental setting

First we describe two experimental settings, denoted as Model 1 and Model 2 below.

Refer to caption
(a) Model 1: unit cell
Refer to caption
(b) Model 1: first Brillouin zone
Refer to caption
(c) Model 2: unit cell
Refer to caption
(d) Model 2: first Brillouin zone
Figure 1: Unit cells and their corresponding first Brillouin zones for Model 1 and Model 2.
Model 1.

There are six silicon blocks layered inside a cubic unit cell Ω=[0,a]3\Omega=[0,a]^{3}, cf. Fig. 1(a) [33]. The relative permittivity ϵ\epsilon inside the silicon blocks is ϵ=13\epsilon=13. Their arrangement leads to xyxy-plane symmetry within the unit cell, but there is no symmetry along the zz-axis. The first Brillouin zone \mathcal{B} is shown in Fig. 1(b), where the high-symmetry points are denoted as Γ=1a(0,0,0)T,X=1a(π,0,0)T,M=1a(π,π,0)T,R=1a(π,π,π)T,T=1a(0,π,π)T,Z=1a(0,0,π)T\Gamma=\frac{1}{a}(0,0,0)^{T},X=\frac{1}{a}(\pi,0,0)^{T},M=\frac{1}{a}(\pi,\pi,0)^{T},R=\frac{1}{a}(\pi,\pi,\pi)^{T},T=\frac{1}{a}(0,\pi,\pi)^{T},Z=\frac{1}{a}(0,0,\pi)^{T}. The IBZ is the polyhedron formed by connecting these high-symmetry points. This type of structure has been successfully applied in preclinical studies [39]. There are eight design parameters {θi}i=18\{\theta_{i}\}_{i=1}^{8}, cf. Fig. 1(a). We fix the block thickness as θ5=θ6=θ7=θ8=0.25a\theta_{5}=\theta_{6}=\theta_{7}=\theta_{8}=0.25a and only seek the optimal parameters {θi}i=14\{\theta_{i}\}_{i=1}^{4} to maximize the band gap over the admissible set

Θad,1:={(θ1,θ2,θ3,θ4):θ1,θ2[0,0.5a] and θ3,θ4[0,a]}.\displaystyle\Theta_{\operatorname{ad},1}:=\left\{(\theta_{1},\theta_{2},\theta_{3},\theta_{4}):\theta_{1},\theta_{2}\in[0,0.5a]\text{ and }\theta_{3},\theta_{4}\in[0,a]\right\}.

It is known that there is a band gap between 4th and 5th bands when θ1=θ2=0.125a\theta_{1}=\theta_{2}=0.125a and θ3=θ4=0.25a\theta_{3}=\theta_{4}=0.25a [6]. Note that the commonly used path to characterize the band function ΓXMRTZΓM\Gamma\to X\to M\to R\to T\to Z\to\Gamma\to M is insufficient to accurately detect band gaps. We will adaptively generate sampling points in the IBZ by Algorithm 1 and approximate band functions element-wisely by Algorithm 2.

Model 2.

The unit cell also contains silicon blocks, cf. Fig. 1(c). Additionally, there is a silicon sphere with a radius of θ4\theta_{4} at the center of the unit cell. This structure imparts symmetry to the unit cell along the x,y,x,y, and zz axes. Therefore, the high-symmetry points in \mathcal{B} are Γ=1a(0,0,0)T\Gamma=\frac{1}{a}(0,0,0)^{T}, X=1a(π,0,0)TX=\frac{1}{a}(\pi,0,0)^{T}, M=1a(π,π,0)TM=\frac{1}{a}(\pi,\pi,0)^{T} and R=1a(π,π,π)TR=\frac{1}{a}(\pi,\pi,\pi)^{T}, and the region enclosed by the lines connecting them represents IBZ. This photonic crystal structure was fabricated in [34] by a sacrificial-scaffold mediated two-photon lithography strategy and using a degradable hydrogel scaffold. There are four design parameters {θi}i=14\{\theta_{i}\}_{i=1}^{4}, which are the dimensions (length and width) of the silicon blocks and the radius of the silicon sphere. We take the following admissible set

Θad,2:={(θ1,θ2,θ3,θ4)[0,0.5a]4:(θi2+θj2)1/2+θ42a/2 for i,j=1,2,3 and ij}\displaystyle\Theta_{\operatorname{ad},2}:=\big{\{}(\theta_{1},\theta_{2},\theta_{3},\theta_{4})\in[0,0.5a]^{4}:(\theta_{i}^{2}+\theta_{j}^{2})^{1/2}+\theta_{4}\leq\sqrt{2}a/2\text{ for }i,j=1,2,3\text{ and }i\neq j\big{\}}

to ensure that there is no overlap between the silicon sphere and the silicon blocks. It can be verified experimentally that there is a band gap when θ1=θ2=θ3=16a\theta_{1}=\theta_{2}=\theta_{3}=\frac{1}{6}a and θ4=1130a\theta_{4}=\frac{11}{30}a.

Next we formulate the band gap maximization problem as PDE constrained optimization. The objective is to determine the unit cell that maximizes the band gap, i.e., the distance between two adjacent band functions ω\omega_{\ell} and ω+1\omega_{\ell+1}. One intuitive way is to define the objective function by

ϕ^1(θ¯;)=min𝐤ω+1(𝐤;θ¯)max𝐤ω(𝐤;θ¯),\hat{\phi}_{1}(\bar{\mathbf{\theta}};\ell)=\min_{\mathbf{k}\in\mathcal{B}}\omega_{\ell+1}(\mathbf{k};\bar{\mathbf{\theta}})-\max_{\mathbf{k}\in\mathcal{B}}\omega_{\ell}(\mathbf{k};\bar{\mathbf{\theta}}), (5.1)

with the design variable vector θ¯:=[θ1,θ2,θ3,θ4]T\bar{\mathbf{\theta}}:=[\theta_{1},\theta_{2},\theta_{3},\theta_{4}]^{T} for shape-wise optimization. Usually, the objective function is normalized by the mean value in which the band gap is turned

ω¯=(min𝐤ω+1(𝐤;θ¯)+max𝐤ω(𝐤;θ¯))/2,\bar{\omega}_{\ell}=\Big{(}\min_{\mathbf{k}\in\mathcal{B}}\omega_{\ell+1}(\mathbf{k};\bar{\mathbf{\theta}})+\max_{\mathbf{k}\in\mathcal{B}}\omega_{\ell}(\mathbf{k};\bar{\mathbf{\theta}})\Big{)}/2, (5.2)

which results in a modified objective function ϕ^2(θ¯;)=ϕ^1(θ¯;)/ω¯\hat{\phi}_{2}(\bar{\mathbf{\theta}};\ell)=\hat{\phi}_{1}(\bar{\mathbf{\theta}};\ell)/\bar{\omega}_{\ell}. We normalized the objective function to indicate how the band gap improves relative to the mean band gap frequency since it is crucial to know not only the band gap width but also its location.

Since each eigenfrequency is obtained by taking the square root of the eigenvalue in equation (2.5), we use the following objective function

ϕ(θ¯;)=min𝐤λ+1,h(𝐤;θ¯)max𝐤λ,h(𝐤;θ¯))(min𝐤λ+1,h(𝐤;θ¯)+max𝐤λ,h(𝐤;θ¯))/2.\phi(\bar{\mathbf{\theta}};\ell)=\frac{\min_{\mathbf{k}\in\mathcal{B}}\lambda_{\ell+1,h}(\mathbf{k};\bar{\mathbf{\theta}})-\max_{\mathbf{k}\in\mathcal{B}}\lambda_{\ell,h}(\mathbf{k};\bar{\mathbf{\theta}}))}{\left(\min_{\mathbf{k}\in\mathcal{B}}\lambda_{\ell+1,h}(\mathbf{k};\bar{\mathbf{\theta}})+\max_{\mathbf{k}\in\mathcal{B}}\lambda_{\ell,h}(\mathbf{k};\bar{\mathbf{\theta}})\right)/2}.

In sum, the optimization problem can then be formulated as

maxθ¯Θad,iϕ(θ¯;) for i=1,2\displaystyle\max_{\bar{\mathbf{\theta}}\in\Theta_{\operatorname{ad},i}}\phi(\bar{\mathbf{\theta}};\ell)\text{ for }i=1,2 (5.3)

subject to the following variational formulation with j=,+1j=\ell,\ell+1,

{a(𝐮j,h,𝐯;𝐤)+b(pj,h,𝐯;𝐤)=λj,h(𝐮j,h,𝐯),𝐯𝐕h𝐤,b(q,𝐮j,h;𝐤)¯=0,qQh𝐤.\left\{\begin{aligned} a(\mathbf{u}_{j,h},\mathbf{v};\mathbf{k})+b(p_{j,h},\mathbf{v};\mathbf{k})&=\lambda_{j,h}(\mathbf{u}_{j,h},\mathbf{v}),\quad\forall\mathbf{v}\in\mathbf{V}_{h}^{\mathbf{k}},\\ \overline{b(q,\mathbf{u}_{j,h};\mathbf{k})}&=0,\qquad\qquad\qquad\forall q\in Q_{h}^{\mathbf{k}}.\end{aligned}\right.
Remark 5.1.

Typically, the unit cell chosen for optimization already possesses some band gaps under certain predefined parameters. For instance, given a specific set of parameters θ¯\bar{\mathbf{\theta}}, there exists a band gap between the 44th and 55th band functions for Model 1, i.e., ϕ(θ¯;4)>0\phi(\bar{\mathbf{\theta}};4)>0. Then we aim to solve (5.3) with :=4\ell:=4. On the contrary, if the unit cell being optimized does not possess any a priori band gaps, we can use the following objective function,

ϕ(θ¯)=maxn=1,,L1min𝐤λn+1(𝐤;θ¯)max𝐤λn(𝐤;θ¯)(min𝐤λn+1(𝐤;θ¯)+max𝐤λn(𝐤;θ¯))/2.\phi(\bar{\mathbf{\theta}})=\max_{n=1,\cdots,L-1}\frac{\min_{\mathbf{k}\in\mathcal{B}}\lambda_{n+1}(\mathbf{k};\bar{\mathbf{\theta}})-\max_{\mathbf{k}\in\mathcal{B}}\lambda_{n}(\mathbf{k};\bar{\mathbf{\theta}})}{\left(\min_{\mathbf{k}\in\mathcal{B}}\lambda_{n+1}(\mathbf{k};\bar{\mathbf{\theta}})+\max_{\mathbf{k}\in\mathcal{B}}\lambda_{n}(\mathbf{k};\bar{\mathbf{\theta}})\right)/2}. (5.4)

It aims to find an optimal parameter θ^\hat{\mathbf{\theta}} that maximizes the band gap among the first LL band functions. By maximizing the difference between the minimum and maximum values of adjacent band functions within the Brillouin zone \mathcal{B}, we can effectively enhance the band gap therebetween.

Next we describe how to solving Problem (5.3), which is divided into two steps. The first step is to evaluate the objective function ϕ(θ¯i;)\phi(\bar{\theta}_{i};\ell) for a given parameter θ¯i\bar{\theta}_{i}, which involves calculating λ,h\lambda_{\ell,h} and λ+1,h\lambda_{\ell+1,h} by Algorithm 2. The second step is to update the parameter from θ¯i\bar{\theta}_{i} to θ¯i+1\bar{\theta}_{i+1} to obtain a larger value in the objective function ϕ(θ¯i+1;)\phi(\bar{\theta}_{i+1};\ell). One repeats these two steps until a certain criterion is attained. Note that solving (5.3) is challenging since it is non-convex and the derivatives of the objective function are neither symbolically nor numerically available. Thus, we adopt some derivative-free algorithms to deal with this problem (5.3). Bayesian optimization (BO) is a popular method for solving optimization problems involving expensive objective functions. Roughly speaking, BO uses a computationally cheap surrogate model to construct a posterior distribution over the original expensive objective ϕ(θ¯;)\phi(\bar{\mathbf{\theta}};\ell) using a limited number of observations. The statistics of the surrogate model are used to sample a new point where the original ϕ(θ¯;)\phi(\bar{\mathbf{\theta}};\ell) is then evaluated. The incorporation of such a surrogate model makes BO an efficient global optimization technique in terms of the number of function evaluations [41, 27, 47, 44]. To the best of our knowledge, the application of BO to photonic band gap maximization remains largely unexplored. In this work, we employ MATLAB’s built-in optimization solver bayesopt. We summarize the procedure in Algorithm 3. For more details about the BO algorithm, we refer to [27, 46].

Algorithm 3 Bayesian optimization procedure
D1={(θ¯1,ϕ(θ¯1;))}D_{1}=\left\{\left(\mathbf{\bar{\theta}}_{1},\phi(\mathbf{\bar{\theta}}_{1};\ell)\right)\right\}: initial guess θ¯1\mathbf{\bar{\theta}}_{1} and its corresponding observation ϕ(θ¯1;)\phi(\mathbf{\bar{\theta}}_{1};\ell) approximated by Algorithm 2; θmini,θmaxi\theta^{i}_{\min},\theta^{i}_{max}, i=1,2,4i=1,2,\cdots 4: minimum and maximum values of the design variables; nmax2n_{\max}^{2}: maximum number of loops.
θ¯\mathbf{\bar{\theta}}
i1i\leftarrow 1
while inmax2i\leq n_{\max}^{2} and ϕ(θ¯i;)>ϕ(θ¯1;)\phi(\mathbf{\bar{\theta}}_{i};\ell)>\phi(\mathbf{\bar{\theta}}_{1};\ell) do select new θ¯i+1\mathbf{\bar{\theta}}_{i+1} by optimization of an acquisition function constructed by DiD_{i} (using bayesopt); get new observation ϕ(θ¯i+1;)\phi(\mathbf{\bar{\theta}}_{i+1};\ell) by Algorithm 2 with nmax1=8n_{\max}^{1}=8 in Algorithm 1; augment data: Di+1={Di,(θ¯i+1,ϕ(θ¯i+1;))}D_{i+1}=\left\{D_{i},\left(\mathbf{\bar{\theta}}_{i+1},\phi(\mathbf{\bar{\theta}}_{i+1};\ell)\right)\right\}; update model; ii+1i\leftarrow i+1;
end while

5.2 Maximization of band gap

Now we solve (5.3) with i=1i=1, =4\ell=4 for Model 1, and i=2i=2, =2\ell=2 for Model 2 by Algorithm 2. We use the lowest order 𝐤\mathbf{k}-modified Nédélec edge elements (2.9) and discretize the computational domain Ω\Omega with a uniform mesh comprised of 24576 tetrahedral elements in Fig. 2(a) for Model 1, 24650 tetrahedral elements in Fig. 2(b) for Model 2. The employed mesh serves dual purposes: first, to fulfill periodic boundary conditions, ensuring a one-to-one correspondence of edges along opposing faces of the cubic domain; second, for Model 2, the tetrahedral mesh necessitates partitioning along the spherical material situated at the center of the cubic domain.

Refer to caption
(a) Model 1
Refer to caption
(b) Model 2
Figure 2: Uniform discretization of the unit cell Ω\Omega. Here, only the mesh inside the silicon blocks and around the interior ball is depicted, and the same mesh size is utilized in the background.

Now we describe the initialization strategy for Algorithm 3.
(i): Model 1. The band structure of the cubic lattice with lattice constant aa, and design variables θ1=θ2=0.125a,θ3=θ4=θ5=θ6=θ7=θ8=0.25a\theta_{1}=\theta_{2}=0.125a,\theta_{3}=\theta_{4}=\theta_{5}=\theta_{6}=\theta_{7}=\theta_{8}=0.25a is initially computed. The resulting band structure shows a band gap between the 44th and 55th bands with normalized band gap width 0.21590.2159. The first six band functions along the high symmetry points are shown in Fig. 3(a). Although there is already a band gap in the initial structure, band gap optimization facilitates a deeper understanding of how their sizes can affect the band gap via parameter optimization.
(ii): Model 2. In case θ1=θ2=θ3=16a\theta_{1}=\theta_{2}=\theta_{3}=\frac{1}{6}a, θ4=1130a\theta_{4}=\frac{11}{30}a, PhCs with such a unit cell can forbid certain frequencies of light. We consider optimizing these four parameters with silicon blocks and sphere inside the unit cell to find a nearly optimal band gap range. The band structure with initial configuration is computed and the resulting band structure shows a band gap between the 22nd and 33rd bands with normalized band gap width 0.056260.05626. The first six band functions along the high symmetry points are shown in Fig. 3(b).

Refer to caption
(a) Model 1 initial band
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(b) Model 2 initial band
Figure 3: Band structures along the high symmetry points.

Next we describe the optimization results of normalized band gap (5.3).

For Model 1, we first fix θ3=θ4=0.25a\theta_{3}=\theta_{4}=0.25a and only optimize (θ1,θ2)(\theta_{1},\theta_{2}), which denotes the width of the four blocks on the 1st and 4th layers. The outcome is shown in Fig. 4(a). Several iterations of Algorithm 3 generate a set of parameters with their band gaps being larger than the known band gap. The optimal values we obtain is (θ1,θ2)=(0.1271a,0.1329a)(\theta_{1},\theta_{2})=(0.1271a,0.1329a) and the maximal normalized band gap is ϕ^2((0.1271a,0.1329a);4)=0.2168\hat{\phi}_{2}((0.1271a,0.1329a);4)=0.2168. This optimization process generates a larger band gap compared to existing literature where the design variables are chosen to be θ1=θ2=0.125a\theta_{1}=\theta_{2}=0.125a and the corresponding normalized band gap is 0.21590.2159. For the optimized parameter, the bandgap width increases by 0.4177%0.4177\%. Further, we optimize parameters (θ1,θ2,θ3,θ4)(\theta_{1},\theta_{2},\theta_{3},\theta_{4}) simultaneously. The results show that when (θ1,θ2,θ3,θ4)=(0.1696a,0.1810a,0.2342a,0.3214a)(\theta_{1},\theta_{2},\theta_{3},\theta_{4})=(0.1696a,0.1810a,0.2342a,0.3214a), one obtains a unit cell design with a larger band gap than the one in the literature. The band gap of the configuration is 0.2220.222, which is 3.0236%3.0236\% higher than the original one. The first six band functions, under these two new sets of design parameters, along the high symmetry points are shown in Fig. 5.

Refer to caption
(a) Model 1: optimize θ1,θ2\theta_{1},\theta_{2}
Refer to caption
(b) Model 2 optimize θ1,,θ4\theta_{1},\cdots,\theta_{4} with θ1=θ2=θ3\theta_{1}=\theta_{2}=\theta_{3}
Figure 4: Bayesian optimization.

For Model 2, we first consider blocks of square cross-section, i.e. θ1=θ2=θ3\theta_{1}=\theta_{2}=\theta_{3} in 1(c) to get a visualized optimization process in Figure 4(b). In this case, Algorithm 3 produces a set of parameters such that the band gap is larger than that of the configuration in reference [34] after a few iterations of parameter selection. The optimal parameters are (θ1,θ2,θ3,θ4)=(0.1707a,0.1707a,0.1707a,0.2190a)(\theta_{1},\theta_{2},\theta_{3},\theta_{4})=(0.1707a,0.1707a,0.1707a,0.2190a) with the normalized band gap is 0.19060.1906, which is 238.8028%238.8028\% higher than the original band gap. Next we optimize parameters (θ1,θ2,θ3,θ4)(\theta_{1},\theta_{2},\theta_{3},\theta_{4}) independently. The results show that (θ1,θ2,θ3,θ4):=0.1798a,0.1777a,0.1842a,0.2992a)(\theta_{1},\theta_{2},\theta_{3},\theta_{4}):=0.1798a,0.1777a,0.1842a,0.2992a) yields a unit cell design with a much larger band gap than the one reported in the literature. The band gap of the configuration is 0.20460.2046, which is 263.6872%263.6872\% higher than the original band gap. The first six band functions along the high symmetry points under these two sets of design parameters are depicted in Fig. 6.

Refer to caption
(a) band structure when optimizing θ1,θ2\theta_{1},\theta_{2}.
Refer to caption
(b) band structure when optimizing θ1,,θ4\theta_{1},\cdots,\theta_{4}.
Figure 5: Model 1: Band structures along the high symmetry points.

During the optimization process, for any generated ”next point”, we adaptively select the sampling points in the first Brillouin zone and element-wisely interpolate λ(𝐤)\lambda_{\ell}(\mathbf{k}) and λ+1(𝐤)\lambda_{\ell+1}(\mathbf{k}). During these four optimization processes, we performed 8 loops of adaptive mesh refinement under each set of parameters. Fig. 7 shows the points used to approximate the objective function under the optimal parameters. We observe that the distribution of the sampling points is not uniform.

Refer to caption
(a) band structure when optimizing θ1,,θ4\theta_{1},\cdots,\theta_{4} with θ1=θ2=θ3\theta_{1}=\theta_{2}=\theta_{3}.
Refer to caption
(b) band structure when optimizing θ1,,θ4\theta_{1},\cdots,\theta_{4} independently.
Figure 6: Model 2: Band structures along the high symmetry points.
Refer to caption
(a) Loop 3
Refer to caption
(b) Loop 4
Refer to caption
(c) Loop 5
Refer to caption
(d) Loop 6
Refer to caption
(e) Loop 7
Refer to caption
(f) Loop 8
Refer to caption
(g) Loop 3
Refer to caption
(h) Loop 4
Refer to caption
(i) Loop 5
Refer to caption
(j) Loop 6
Refer to caption
(k) Loop 7
Refer to caption
(l) Loop 8
Figure 7: Sampling points produced by Algorithm 1 for Model 1 with (θ1,θ2,θ3,θ4)=(0.1696a,0.1810a,0.2342a,0.3214a)(\theta_{1},\theta_{2},\theta_{3},\theta_{4})=(0.1696a,0.1810a,0.2342a,0.3214a). (a)-(f): side view; (g)-(l): top view. We present a zoomed-in view in (c)-(f) for a better illustration.
Refer to caption
(a) Model 1 with optimal θ1,,θ4\theta_{1},\cdots,\theta_{4}.
Refer to caption
(b) Model 1 with optimal θ1,,θ4\theta_{1},\cdots,\theta_{4}.
Refer to caption
(c) Model 1 with optimal θ1,θ2\theta_{1},\theta_{2}.
Refer to caption
(d) Model 1 with optimal θ1,θ2\theta_{1},\theta_{2}.
Refer to caption
(e) Model 2 with optimal θ1,,θ4\theta_{1},\cdots,\theta_{4}.
Refer to caption
(f) Model 2 with optimal θ1,,θ4\theta_{1},\cdots,\theta_{4}.
Refer to caption
(g) Model 2 with optimal θ1=θ2=θ3,θ4\theta_{1}=\theta_{2}=\theta_{3},\theta_{4}.
Refer to caption
(h) Model 2 with optimal θ1=θ2=θ3,θ4\theta_{1}=\theta_{2}=\theta_{3},\theta_{4}.
Figure 8: Maximum (left) and average (right) relative error with respect to the number of sampling points.

5.3 Efficiency of Algorithm 2

Finally, we show the efficiency of Algorithm 2 by comparing with uniform mesh refinement with lowest-order element-wise interpolation under the optimal parameters. In both models, we randomly choose 5000 𝐤\mathbf{k}-points in the IBZ (denoted by ^\hat{\mathcal{B}}), on which we compute the reference solution using the 𝐇(curl)\mathbf{H}(\text{curl})-conforming 𝐤\mathbf{k}-modified Nédélec edge FEM (2.9) with the mesh in Fig. 2. The pointwise relative error is then computed on ^\hat{\mathcal{B}} by

ei(𝐤):=|ωi(𝐤)ω^i(𝐤)|ωi(𝐤) for 𝐤^,\displaystyle e_{i}(\mathbf{k}):=\frac{|\omega_{i}(\mathbf{k})-\hat{\omega}_{i}(\mathbf{k})|}{{\omega_{i}}(\mathbf{k})}\quad\text{ for }\mathbf{k}\in\hat{\mathcal{B}},

for some certain band number ii. Here, ωi(𝐤)\omega_{i}(\mathbf{k}) is the iith reference band function obtained directly by the 𝐇(curl)\mathbf{H}(\text{curl})-conforming 𝐤\mathbf{k}-modified Nédélec edge FEM using the same mesh on the unit cell Ω\Omega, and ω^i(𝐤)\hat{\omega}_{i}(\mathbf{k}) is obtained by the element-wise polynomial interpolation. We measure the accuracy of the methods using maximum and average relative errors

error:=maxi𝕄qmax𝐤^|ei(𝐤)|anderroravg:=averagei𝕄qmax𝐤^|ei(𝐤)|,\displaystyle\operatorname{error}_{\infty}:=\max_{i\in\mathbb{M}_{q}}\max_{\mathbf{k}\in\hat{\mathcal{B}}}\left|e_{i}(\mathbf{k})\right|\quad\mbox{and}\quad\operatorname{error}_{\text{avg}}:=\text{average}_{i\in\mathbb{M}_{q}}\max_{\mathbf{k}\in\hat{\mathcal{B}}}\left|e_{i}(\mathbf{k})\right|,

where q{1,2}q\in\{1,2\}, 𝕄1:={4,5}\mathbb{M}_{1}:=\{4,5\} for Model 1 and 𝕄2:={2,3}\mathbb{M}_{2}:=\{2,3\} for Model 2.

We depict in Fig. 7 the distribution of the sampling points for Model 1 with four optimizing parameters obtained from Algorithm 1. We observe that the sampling points are not uniformly distributed. Moreover, Fig. 8 presents the relative errors with respect to the number of sampling points, where error\operatorname{error}_{\infty} and erroravg\operatorname{error}_{\text{avg}} versus NN, i.e., the number of sampling points, on log-log coordinates with κ=22\kappa=2\sqrt{2} and μ=1\mu=1 are plotted. We observe that Algorithm 2 has a much faster convergence rate than the uniform one. Further, compared with the reference lines, Algorithm 2 has an approximately first-order convergence rate, while the uniform method is much less efficient with an approximately 13\frac{1}{3}-order convergence rate, which is consistent with Theorem 4.6 and Remark 4.1.

6 Conclusions

In this work, we present a generalization of an hphp-adaptive sampling method for computing the band functions of three-dimensional photonic crystals with an application in the band gap maximization. We present theoretical analysis and numerical tests to verify its performance. For the band gap maximization problem, we generate an adaptive mesh in the parameter domain each time when the geometry of the photonic crystal is updated. One intriguing question is whether one can use the same adaptive mesh for a few updates in the geometry of the photonic crystals in order to make our algorithm more efficient. Also, it is of interest to have more freedom in constructing photonic crystals with an infinite number of design parameters, e.g., the interface between two different materials can be designed.

References

  • [1] I. Babuška and M. Suri. The pp and hphp versions of the finite element method, basic principles and properties. SIAM Rev., 36(4):578–632, 1994.
  • [2] Z. Bai, J. Demmel, J. Dongarra, A. Ruhe, and H. van der Vorst. Templates for the Solution of Algebraic Eigenvalue Problems: a Practical Guide. SIAM, Philadelphia, PA, 2000.
  • [3] M. Blyth and C. Pozrikidis. A Lobatto interpolation grid over the triangle. IMA J. Appl. Math., 71(1):153–169, 2006.
  • [4] D. Boffi, M. Conforti, and L. Gastaldi. Modified edge finite elements for photonic crystals. Numer. Math., 105:249–266, 2006.
  • [5] D. Boffi, P. Fernandes, L. Gastaldi, and I. Perugia. Computational models of electromagnetic resonators: analysis of edge element approximation. SIAM J. Numer. Anal., 36(4):1264–1290, 1999.
  • [6] A. Bulovyatov. A parallel multigrid method for band structure computation of 3D photonic crystals with higher order finite elements. PhD thesis, Karlsruhe Institute of Technology, Karlsruhe, 2010.
  • [7] J. Chan and T. Warburton. A comparison of high order interpolation nodes for the pyramid. SIAM J. Sci. Comput., 37(5):A2151–A2170, 2015.
  • [8] F. Chen, S. Wang, and H. Nasrabadi. Molecular simulation of competitive adsorption of hydrogen and methane: Analysis of hydrogen storage feasibility in depleted shale gas reservoirs. In SPE Reservoir Simulation Conference. OnePetro, 2023.
  • [9] L. Chen. iiFEM: an integrated finite element methods package in MATLAB. Technical report, 2009.
  • [10] Y. Chen, Z. Lan, and J. Zhu. Inversely designed second-order photonic topological insulator with multiband corner states. Phys. Rev. Appl., 17(5):054003, 2022.
  • [11] S.-H. Chou, T.-M. Huang, T. Li, J.-W. Lin, and W.-W. Lin. A finite element based fast eigensolver for three dimensional anisotropic photonic crystals. J. Comput. Phys., 386:611–631, 2019.
  • [12] A. Chutinan, S. John, and O. Toader. Diffractionless Flow of Light in All-Optical Microchips. Phys. Rev. Lett., 90(4):123901, 2003.
  • [13] R. Craster, T. Antonakakis, M. Makwana, and S. Guenneau. Dangers of using the edges of the Brillouin zone. Phys. Rev. B, 86(11):115130, 2012.
  • [14] C. Dobson, J. Gopalakrishnan, and E. Pasciak. An efficient method for band structure calculations in 3D photonic crystals. J. Comput. Phys., 161(2):668–679, 2000.
  • [15] C. Dobson and E. Pasciak. Analysis of an algorithm for computing electromagnetic Bloch modes using Nédélec spaces. Comput. Methods Appl. Math., 1(2):138–153, 2001.
  • [16] H.-W. Dong, X.-X. Su, Y.-S. Wang, and C. Zhang. Topology optimization of two-dimensional asymmetrical phononic crystals. Phys. Lett. A, 378(4):434–441, 2014.
  • [17] H.-W. Dong, Y.-S. Wang, Y.-F. Wang, and C. Zhang. Reducing symmetry in topology optimization of two-dimensional porous phononic crystals. Aip Adv., 5(11), 2015.
  • [18] L. Dong, A. Mckay, A. Marcinkevicius, L. Fu, J. Li, K. Thomas, and E. Fermann. Extending effective area of fundamental mode in optical fibers. J. Lightwave Technol., 27(11):1565–1570, 2009.
  • [19] T. Eidam, J. Rothhardt, F. Stutzki, F. Jansen, S. Hädrich, H. Carstens, C. Jauregui, J. Limpert, and A. Tünnermann. Fiber chirped-pulse amplification system emitting 3.8 GW peak power. Optics Expr., 19(1):255–260, 2011.
  • [20] A. Gazonas, S. Weile, R. Wildman, and A. Mohan. Genetic algorithm optimization of phononic bandgap structures. Int. J. Solids Struct., 43(18-19):5851–5866, 2006.
  • [21] I. M. Glazman. Direct Methods of Qualitative Spectral Analysis of Singular Differential Operators. Israel Program for Scientific Translations, 1965.
  • [22] M. Hammond, A. Oskooi, M. Chen, Z. Lin, G. Johnson, and E. Ralph. High-performance hybrid time/frequency-domain topology optimization for large-scale photonics inverse design. Optics Expr., 30(3):4467–4491, 2022.
  • [23] J. Harrison, P. Kuchment, A. Sobolev, and B. Winn. On occurrence of spectral edges for periodic operators inside the Brillouin zone. J. Phys. A: Math. Theor., 40(27):7597, 2007.
  • [24] W.-Q. Huang, W.-W. Lin, H.-S. Lu, and S.-T. Yau. iSIRA: Integrated shift–invert residual Arnoldi method for graph Laplacian matrices from big data. J. Comput. Appl. Math., 346:518–531, 2019.
  • [25] D. Jackson. Classical Electrodynamics. American Association of Physics, New York, 1999.
  • [26] J. D. Joannopoulos, S. G. Johnson, J. N. Winn, and R. D. Meade. Photonic Crystals: Molding the Flow of Light. Princeton Univ. Press, Princeton, NJ, 2008.
  • [27] R. Jones, M. Schonlau, and J. Welch. Efficient global optimization of expensive black-box functions. J. Global optim., 13:455–492, 1998.
  • [28] P. Kuchment. Floquet Theory for Partial Differential Equations. Springer, Basel, 1993.
  • [29] D. Labilloy, H. Benisty, C. Weisbuch, T. Krauss, V. Bardinal, and U. Oesterle. Demonstration of cavity mode between two-dimensional photonic-crystal mirrors. Elec. Lett., 33(23):1978–1980, 1997.
  • [30] V. Laude. Phononic Crystals. de Gruyter, Berlin/Boston, 2002.
  • [31] J.-F. Lee, D. Sun, and Z. Cendes. Tangential vector finite elements for electromagnetic field computation. IEEE Trans. Magnet., 27(5):4032–4035, 1991.
  • [32] H. Li, G. Ren, Y. Gao, B. Zhu, J. Wang, B. Yin, and S. Jian. Hollow-core photonic bandgap fibers for orbital angular momentum applications. J. Optics, 19(4):045704, 2017.
  • [33] S.-y. Lin, J. Fleming, D. Hetherington, B. Smith, R. Biswas, K. Ho, M. Sigalas, W. Zubrzycki, S. Kurtz, and J. Bur. A three-dimensional photonic crystal operating at infrared wavelengths. Nature, 394(6690):251–253, 1998.
  • [34] K. Liu, H. Ding, S. Li, Y. Niu, Y. Zeng, J. Zhang, X. Du, and Z. Gu. 3d printing colloidal crystal microstructures via sacrificial-scaffold-mediated two-photon lithography. Nature Comm., 13(1):4563, 2022.
  • [35] Z. Lu, A. Çesmelioglu, J. Van der Vegt, and Y. Xu. Discontinuous Galerkin approximations for computing electromagnetic Bloch modes in photonic crystals. J. Sci. Comput., 70(2):922–964, 2017.
  • [36] Z. Lu and Y. Xu. A parallel eigensolver for photonic crystals discretized by edge finite elements. J. Sci. Comput., 92(3):79, 2022.
  • [37] H. Luo and C. Pozrikidis. A Lobatto interpolation grid in the tetrahedron. IMA J. Appl. Math., 71(2):298–313, 2006.
  • [38] X.-l. Lyu, T. Li, T.-m. Huang, J.-w. Lin, W.-w. Lin, and S. Wang. FAME: fast algorithms for Maxwell’s equations for three-dimensional photonic crystals. ACM Trans. Math. Software (TOMS), 47(3):1–24, 2021.
  • [39] J. Mačiulaitis, M. Deveikytė, S. Rekštytė, M. Bratchikov, A. Darinskas, A. Šimbelytė, G. Daunoras, A. Laurinavičienė, A. Laurinavičius, R. Gudas, et al. Preclinical study of SZ2080 material 3D microstructured scaffolds for cartilage tissue engineering made by femtosecond direct laser writing lithography. Biofabrication, 7(1):015015, 2015.
  • [40] F. Maurin, C. Claeys, E. Deckers, and W. Desmet. Probability that a band-gap extremum is located on the irreducible Brillouin-zone contour for the 17 different plane crystallographic lattices. Int. J. Solids Struct., 135:26–36, 2018.
  • [41] J. Mockus. Application of Bayesian approach to numerical methods of global and stochastic optimization. J. Global Optim., 4:347–365, 1994.
  • [42] M. Rivara and C. Levin. a 3-d refinement algorithm suitable for adaptive and multi-grid techniques. Comm. Appl. Numer. Methods, 8:281–290, 1992.
  • [43] P. Russell. Photonic crystal fibers. Science, 299(5605):358–362, 2003.
  • [44] J. Sasena. Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. University of Michigan, Ann Arbor, Michigan, 2002.
  • [45] C. Schwab. pp-and hphp-Finite Element Methods: Theory and Applications in Solid and Fluid Mechanics. Oxford University Press, Oxford, 1998.
  • [46] B. Shahriari, K. Swersky, Z. Wang, P. Adams, and N. De Freitas. Taking the human out of the loop: A review of Bayesian optimization. Proceedings of the IEEE, 104(1):148–175, 2015.
  • [47] S. Streltsov and P. Vakili. A non-myopic utility function for statistical global optimization algorithms. J. Global Optim., 14:283–298, 1999.
  • [48] D. Sun, J. Manges, X. Yuan, and Z. Cendes. Spurious modes in finite-element methods. IEEE Antennas Prop. Mag., 37(5):12–24, 1995.
  • [49] S. Wang, S. Li, and Y.-S. Wu. An analytical solution of pressure and displacement induced by recovery of poroelastic reservoirs and its applications. SPE J., 28(03):1329–1348, 2023.
  • [50] Y. Wang and G. Li. An hphp-adaptive sampling algorithm on dispersion relation reconstruction for 2D photonic crystals. arXiv preprint, arXiv:2311.16454, 2023.
  • [51] Y. Wang and G. Li. Dispersion relation reconstruction for 2D photonic crystals based on polynomial interpolation. J. Comput. Phys., 498:112659, 2024.
  • [52] A. Woldering, P. Mosk, and L. Vos. Design of a three-dimensional photonic band gap cavity in a diamondlike inverse woodpile photonic crystal. Phys. Rev. B, 90(9):115140, 2014.
  • [53] Y. Yan, P. Liu, X. Zhang, and Y. Luo. Photonic crystal topological design for polarized and polarization-independent band gaps by gradient-free topology optimization. Optics Expr., 29(16):24861–24883, 2021.
  • [54] F. Yanik, S. Fan, M. Soljačić, and D. Joannopoulos. All-optical transistor action with bistable switching in a photonic crystal cross-waveguide geometry. Optics Lett., 28(24):2506–2508, 2003.