Computer Physics Communications最新文献

{"title":"GPU-enabled extreme-scale turbulence simulations: Fourier pseudo-spectral algorithms at the exascale using OpenMP offloading","authors":"P.K. Yeung , Kiran Ravikumar , Stephen Nichols , Rohini Uma-Vaideswaran","doi":"10.1016/j.cpc.2024.109364","DOIUrl":"10.1016/j.cpc.2024.109364","url":null,"abstract":"<div><p>Fourier pseudo-spectral methods for nonlinear partial differential equations are of wide interest in many areas of advanced computational science, including direct numerical simulation of three-dimensional (3-D) turbulence governed by the Navier-Stokes equations in fluid dynamics. This paper presents a new capability for simulating turbulence at a new record resolution up to 35 trillion grid points, on the world's first exascale computer, <em>Frontier</em>, comprising AMD MI250x GPUs with HPE's Slingshot interconnect and operated by the US Department of Energy's Oak Ridge Leadership Computing Facility (OLCF). Key programming strategies designed to take maximum advantage of the machine architecture involve performing almost all computations on the GPU which has the same memory capacity as the CPU, performing all-to-all communication among sets of parallel processes directly on the GPU, and targeting GPUs efficiently using OpenMP offloading for intensive number-crunching including 1-D Fast Fourier Transforms (FFT) performed using AMD ROCm library calls. With 99% of computing power on Frontier being on the GPU, leaving the CPU idle leads to a net performance gain via avoiding the overhead of data movement between host and device except when needed for some I/O purposes. Memory footprint including the size of communication buffers for MPI_ALLTOALL is managed carefully to maximize the largest problem size possible for a given node count.</p><p>Detailed performance data including separate contributions from different categories of operations to the elapsed wall time per step are reported for five grid resolutions, from 2048<sup>3</sup> on a single node to 32768<sup>3</sup> on 4096 or 8192 nodes out of 9408 on the system. Both 1D and 2D domain decompositions which divide a 3D periodic domain into slabs and pencils respectively are implemented. The present code suite (labeled by the acronym GESTS, GPUs for Extreme Scale Turbulence Simulations) achieves a figure of merit (in grid points per second) exceeding goals set in the Center for Accelerated Application Readiness (CAAR) program for Frontier. The performance attained is highly favorable in both weak scaling and strong scaling, with notable departures only for 2048<sup>3</sup> where communication is entirely intra-node, and for 32768<sup>3</sup>, where a challenge due to small message sizes does arise. Communication performance is addressed further using a lightweight test code that performs all-to-all communication in a manner matching the full turbulence simulation code. Performance at large problem sizes is affected by both small message size due to high node counts as well as dragonfly network topology features on the machine, but is consistent with official expectations of sustained performance on Frontier. Overall, although not perfect, the scalability achieved at the extreme problem size of 32768<sup>3</sup> (and up to 8192 nodes — which corresponds to hardware rated at just under 1 exa","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109364"},"PeriodicalIF":7.2,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Universally Adaptable Multiscale Molecular Dynamics (UAMMD). A native-GPU software ecosystem for complex fluids, soft matter, and beyond","authors":"Raúl P. Peláez , Pablo Ibáñez-Freire , Pablo Palacios-Alonso , Aleksandar Donev , Rafael Delgado-Buscalioni","doi":"10.1016/j.cpc.2024.109363","DOIUrl":"10.1016/j.cpc.2024.109363","url":null,"abstract":"<div><p>We introduce UAMMD (Universally Adaptable Multiscale Molecular Dynamics), a novel software infrastructure tailored for mesoscale complex fluid simulations on GPUs. The UAMMD library encompasses a comprehensive range of computational schemes optimized for the GPU, spanning from molecular dynamics to immersed boundary fluctuating-hydrodynamics. Developed in CUDA/C++14, this header-only open-source software serves both as a simulation engine and as a library with a modular architecture, offering a vast array of independent modules, categorized as <em>interactors</em> (neighbor search, bonded, non-bonded and electrostatic interactions, etc.) and <em>integrators</em> (molecular dynamics, dissipative particle dynamics, smooth particle hydrodynamics, Brownian hydrodynamics and a rather complete array of Immersed Boundary -IB- schemes). UAMMD excels in schemes that couple particle-based elastic structures with continuum fields in different regions of the mesoscale. To that end, thermal fluctuations can be added in physically consistent ways, and fast modes can be eliminated to adapt UAMMD to different regimes (compressible or incompressible flow, inertial or Stokesian dynamics, etc.). Thus, UAMMD is extremely useful for coarse-grained simulations of nanoparticles, and soft and biological matter (from proteins to viruses and micro-swimmers). Importantly, all UAMMD developments are hand-to-hand validated against experimental techniques, and it has proven to <em>quantitatively</em> reproduce experimental signals from quartz-crystal microbalance, atomic force microscopy, magnetic sensors, optic-matter interaction and ultrasound.</p></div><div><h3>Program summary</h3><p><em>Program Title:</em> UAMMD</p><p><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/srrt2y5s4m.1</span><svg><path></path></svg></span></p><p><em>Developer's repository link:</em> <span><span>https://github.com/RaulPPelaez/UAMMD/</span><svg><path></path></svg></span></p><p><em>Licensing provisions:</em> GPLv3</p><p><em>Programming language:</em> C++/CUDA</p><p><em>Nature of problem:</em> The key problem addressed in computational physics is simulating the behavior of matter at various scales, encompassing both discrete (particle-based) and continuum (field-based) approaches. The challenge lies in accurately and efficiently modeling interactions at different spatio-temporal scales, ranging from atomic (microscopic) to fluid dynamics (macroscopic). This complexity is further amplified in mesoscale regions, where different physics domains intersect, necessitating advanced computational techniques to capture the nuanced dynamics of systems such as colloids, polymers, and biological structures.</p><p><em>Solution method:</em> The present solution consists in the creation of UAMMD (Universally Adaptable Multiscale Molecular Dynamics), a CUDA/C++14 library designed for GPU-accelerated complex fluid simulations. UAMMD offers a flexible platform that integrates d","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109363"},"PeriodicalIF":7.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions","authors":"Sibo Cheng ,&nbsp;Jinyang Min ,&nbsp;Che Liu ,&nbsp;Rossella Arcucci","doi":"10.1016/j.cpc.2024.109359","DOIUrl":"10.1016/j.cpc.2024.109359","url":null,"abstract":"<div><p>Data assimilation techniques are often confronted with challenges handling complex high dimensional physical systems, because high precision simulation in complex high dimensional physical systems is computationally expensive and the exact observation functions that can be applied in these systems are difficult to obtain. It prompts growing interest in integrating deep learning models within data assimilation workflows, but current software packages for data assimilation cannot handle deep learning models inside. This study presents a novel Python package seamlessly combining data assimilation with deep neural networks to serve as models for state transition and observation functions. The package, named TorchDA, implements Kalman Filter, Ensemble Kalman Filter (EnKF), 3D Variational (3DVar), and 4D Variational (4DVar) algorithms, allowing flexible algorithm selection based on application requirements. Comprehensive experiments conducted on the Lorenz 63 and a two-dimensional shallow water system demonstrate significantly enhanced performance over standalone model predictions without assimilation. The shallow water analysis validates data assimilation capabilities mapping between different physical quantity spaces in either full space or reduced order space. Overall, this innovative software package enables flexible integration of deep learning representations within data assimilation, conferring a versatile tool to tackle complex high dimensional dynamical systems across scientific domains.</p></div><div><h3>Program summary</h3><p><em>Program Title:</em> TorchDA</p><p><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/bm5d7xk6gw.1</span><svg><path></path></svg></span></p><p><em>Developer's repository link:</em> <span><span>https://github.com/acse-jm122/torchda</span><svg><path></path></svg></span></p><p><em>Licensing provisions:</em> GNU General Public License version 3</p><p><em>Programming language:</em> Python3</p><p><em>External routines/libraries:</em> Pytorch.</p><p><em>Nature of problem:</em> Deep learning has recently emerged as a potent tool for establishing data-driven predictive and observation functions within data assimilation workflows. Existing data assimilation tools like OpenDA and ADAO are not well-suited for handling predictive and observation models represented by deep neural networks. This gap necessitates the development of a comprehensive package that harmonizes deep learning and data assimilation.</p><p><em>Solution method:</em> This project introduces TorchDA, a novel computational tool based on the PyTorch framework, addressing the challenges posed by predictive and observation functions represented by deep neural networks. It enables users to train their custom neural networks and effortlessly incorporate them into data assimilation processes. This integration facilitates the incorporation of real-time observational data in both full and reduced physical spaces.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109359"},"PeriodicalIF":7.2,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0010465524002820/pdfft?md5=cf361b3ec606c0abf6dee1adecdebe6d&pid=1-s2.0-S0010465524002820-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid Eulerian-Lagrangian Vlasov method for nonlinear wave-particle interaction in weakly inhomogeneous magnetic field","authors":"Jiangshan Zheng ,&nbsp;Ge Wang ,&nbsp;Bo Li","doi":"10.1016/j.cpc.2024.109362","DOIUrl":"10.1016/j.cpc.2024.109362","url":null,"abstract":"<div><p>We present a hybrid Eulerian-Lagrangian (HEL) Vlasov method for nonlinear resonant wave-particle interactions in weakly inhomogeneous magnetic field. The governing Vlasov equation is derived from a recently proposed resonance tracking Hamiltonian theory. It gives the evolution of the distribution function with a scale-separated Hamiltonian that contains the fast-varying coherent wave-particle interaction and slowly-varying motion about the resonance frame of reference. The hybrid scheme solves the fast-varying phase space evolution on Eulerian grid with an adaptive time step and then advances the slowly-varying dynamics by Lagrangian method along the resonance trajectory. We apply the HEL method to study the frequency chirping of whistler-mode chorus wave in the magnetosphere and the self-consistent simulations reproduce the chirping chorus wave and give high-resolution phase space dynamics of energetic particles at low computational cost. The scale-separated HEL approach could provide additional insights of the wave instabilities and wave-particle nonlinear coherence compared to the conventional Vlasov and particle-in-cell methods.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109362"},"PeriodicalIF":7.2,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142148820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and geometrization in PGNAA","authors":"Halisson Alberdan Cavalcanti Cardoso ,&nbsp;Silvio de Barros Melo ,&nbsp;Ilker Meric","doi":"10.1016/j.cpc.2024.109360","DOIUrl":"10.1016/j.cpc.2024.109360","url":null,"abstract":"<div><p>Prompt Gamma Neutron Analysis Activation is a widely used technique for analyzing materials. This technique defines graphs (reference spectrum collection, or libraries) of spectral intensity as a function of energy (channels) for the elements inserted in a sample. The Monte Carlo Library Least Squares (MCLLS) is the dominant approach in the PGNAA technique. The main difficulties faced in the MCLLS domain are (1) numerical instabilities in the least-squares stage (Library Least Squares (LLS)); (2) overdetermination of the system of equations; (3) linear dependence in the libraries; (4) gamma radiation scattering; (5) high computational costs. The present work proposes optimizing the LLS module to face the abovementioned problems using the Greedy Randomized Adaptive Search Procedure (GRASP) and Continuous Greedy Randomized Adaptive Search Procedure (CGRASP) algorithms. The search for the spectral count peaks of the libraries leads to a partitioning of the data before applying the GRASP and CGRASP algorithms. The methodological procedures also address estimating the spectral counts of an unknown library possibly integrates the sample. The results show (1) efficient partitioning of the input data (2) evidence of suitable precision of the weight fractions of the libraries that make up the sample (average precision of the order of 3.16% against 8.8% of other methods); (3) success in the approximation and estimation of the unknown library (average precision of 4.25%) present in the sample. Our method proved to be promising in improving the determination of percentage count fractions by the least-squares module and showing the advantages of data partitioning.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109360"},"PeriodicalIF":7.2,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142135953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Python tool for parameter estimation of “black box” macro- and micro-kinetic models with Bayesian optimization – petBOA","authors":"Sashank Kasiraju ,&nbsp;Yifan Wang ,&nbsp;Saurabh Bhandari ,&nbsp;Aayush R. Singh ,&nbsp;Dionisios G. Vlachos","doi":"10.1016/j.cpc.2024.109358","DOIUrl":"10.1016/j.cpc.2024.109358","url":null,"abstract":"<div><p>We develop an open-source Python-based Parameter Estimation Tool utilizing Bayesian Optimization (petBOA) with a unique wrapper interface for gradient-free parameter estimation of expensive black-box kinetic models. We provide examples for Python macrokinetic and microkinetic modeling (MKM) tools, such as Cantera and OpenMKM. petBOA leverages surrogate Gaussian processes to approximate and minimize the objective function designed for parameter estimation. Bayesian Optimization (BO) is implemented using the open-source BoTorch toolkit. petBOA employs local and global sensitivity analyses to identify important parameters optimized against experimental data, and leverages pMuTT for consistent kinetic and thermodynamic parameters while perturbing species binding energies within the typical error of conventional DFT exchange-correlation functionals (20-30 kJ/mol). The source code and documentation are hosted on GitHub.</p></div><div><h3>Program summary</h3><p><em>Program title</em>: petBOA</p><p><em>Developer's repository link</em>: <span><span>https://github.com/VlachosGroup/petBOA</span><svg><path></path></svg></span></p><p><em>Licensing provisions</em>: MIT license</p><p><em>Programming language</em>: Python</p><p><em>External routines</em>: NEXTorch, PyTorch, GPyTorch, BoTorch, Matplotlib, PyDOE2, NumPy, SciPy, pandas, pMuTT, SALib, docker.</p><p><em>Nature of the problem</em>: An open-source, gradient-free parameter estimation of black-box microkinetic modeling tools, such as OpenMKM is lacking.</p><p><em>Solution method</em>: petBOA is a Python-based tool that utilizes Bayesian Optimization and offers a unique wrapper interface for expensive black-box kinetic models. It leverages the pMuTT library for consistent kinetic and thermodynamic parameter estimation and employs both local and global sensitivity analyses to identify crucial parameters.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109358"},"PeriodicalIF":7.2,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142163978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CoupledElectricMagneticDipoles.jl - Julia modules for coupled electric and magnetic dipoles method for light scattering, and optical forces in three dimensions","authors":"Augustin Muster,&nbsp;Diego R. Abujetas,&nbsp;Frank Scheffold,&nbsp;Luis S. Froufe-Pérez","doi":"10.1016/j.cpc.2024.109361","DOIUrl":"10.1016/j.cpc.2024.109361","url":null,"abstract":"<div><p>CoupledElectricMagneticDipoles.jl is a set of modules implemented in the Julia language. Several modules are provided to solve typical problems encountered in nano-optics and nano-photonics including light emission by point sources in complex environments, electromagnetic wave scattering by single objects with complex geometry or collections of them. Optical forces can also be computed with this software package.</p><p>Two closely related computational methods are implemented in this library, the discrete dipole approach (DDA) and the coupled electric and magnetic dipoles (CEMD) method.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109361"},"PeriodicalIF":7.2,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0010465524002844/pdfft?md5=f3f950fa5a78f74712f99c9054fb7264&pid=1-s2.0-S0010465524002844-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142243051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FeynCalc 10: Do multiloop integrals dream of computer codes?","authors":"Vladyslav Shtabovenko ,&nbsp;Rolf Mertig ,&nbsp;Frederik Orellana","doi":"10.1016/j.cpc.2024.109357","DOIUrl":"10.1016/j.cpc.2024.109357","url":null,"abstract":"<div><p>In this work we report on a new version of <span>FeynCalc</span>, a <span>Mathematica</span> package widely used in the particle physics community for manipulating quantum field theoretical expressions and calculating Feynman diagrams. Highlights of the new version include greatly improved capabilities for doing multiloop calculations, including topology identification and minimization, optimized tensor reduction, rewriting of scalar products in terms of inverse denominators, detection of equivalent or scaleless loop integrals, derivation of Symanzik polynomials, Feynman parametric as well as graph representation for master integrals and initial support for handling differential equations and iterated integrals. In addition to that, the new release also features completely rewritten routines for color algebra simplifications, inclusion of symmetry relations between arguments of Passarino–Veltman functions, tools for determining matching coefficients and quantifying the agreement between numerical results, improved export to <figure><img></figure> and first steps towards a better support of calculations involving light-cone vectors.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109357"},"PeriodicalIF":7.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0010465524002807/pdfft?md5=8047aa302e8233ef39f01de3a0e003f1&pid=1-s2.0-S0010465524002807-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Particle-based modeling and GPU-accelerated simulation of cellular blood flow","authors":"Zehong Xia,&nbsp;Ziwei Zhu,&nbsp;Ting Ye,&nbsp;Ni Sun","doi":"10.1016/j.cpc.2024.109350","DOIUrl":"10.1016/j.cpc.2024.109350","url":null,"abstract":"<div><p>Computational modeling and simulation of cellular blood flow is highly desired for understanding blood microcirculation and blood-related diseases such as thrombosis and tumor, but it remains a challenging task primarily because blood in microvessels should be described as a dense suspension of different types of deformable cells. The focus of the present work is on the development of a particle-based and GPU-accelerated numerical method that is able to quickly simulate the various behaviors of deformable cells in three-dimensional arbitrarily complex geometries. We employ a two-fluid model to describe blood flow, incorporating the deformation and aggregation of cells. A smoothed dissipative particle dynamics is used to solve the two-fluid model, and a discrete microstructure model is applied for the cell deformation, as well as a Morse potential model for the cell aggregation. The heterogeneous CPU-GPU environment is established, where each GPU thread is dedicated to a particle, and the CPU is mainly responsible for loading and exporting data. Five test cases are conducted against analytical theory, experimental data, and previous numerical results, for pure fluid, cell deformation, cell aggregation, cell suspension and the cellular flow in a complex network, respectively. It is shown that the methodology can accurately predict various behaviors of cells, and the GPU is well suited for particle-based modeling. Especially for cellular blood flow, where calculating cellular forces is a compute-intensive and time-consuming task, the GPU offers exceptional parallel capabilities, significantly enhancing the simulation efficiency. The speedup is about 3.5 times faster than the CPU parallelization with 96 cores for the pure fluid, and this acceleration nearly reaches 20 times when cells are included in the simulations. Particularly, the calculations for deformation and aggregation forces demonstrate a substantial speedup, achieving the improvements of up to 120 and 640 times, respectively, compared to their serial counterparts. The present methodology can effectively integrate various behaviors of cells, and has the potential in simulating very large microvascular networks at organ levels.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109350"},"PeriodicalIF":7.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RCWA4D: Electromagnetic solver for layered structures with incommensurate periodicities","authors":"Beicheng Lou ,&nbsp;Shanhui Fan","doi":"10.1016/j.cpc.2024.109356","DOIUrl":"10.1016/j.cpc.2024.109356","url":null,"abstract":"<div><p>We describe RCWA4D, an electromagnetic solver for layered structures with incommensurate periodicities. Our method is based on an extension of the rigorous coupled wave analysis. We illustrate our method on the example of twisted bilayer photonic crystal and show that various properties of such structures can be reliably simulated. The method can be generalized to multi-layer structures in general in which each layer is periodic or quasi-periodic.</p></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"306 ","pages":"Article 109356"},"PeriodicalIF":7.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}