Computer Physics Communications最新文献_第2页

Accurate-geometry-embodied finite element method for phonon Boltzmann transport equation 声子玻尔兹曼输运方程的精确几何体现有限元法

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-15 DOI: 10.1016/j.cpc.2025.109623

Dingtao Shen , Wei Su

{"title":"Accurate-geometry-embodied finite element method for phonon Boltzmann transport equation","authors":"Dingtao Shen , Wei Su","doi":"10.1016/j.cpc.2025.109623","DOIUrl":"10.1016/j.cpc.2025.109623","url":null,"abstract":"<div><div>Modeling nano- and micro-scale heat conduction based on the phonon Boltzmann transport equation has gained increasing research interest due to the demand for better thermal performance of semiconductors. Nevertheless, the high dimensionality of the Boltzmann equation results in the so-called curse of dimensionality, presenting a bottleneck for efficient numerical solutions based on direct discretization. In practice, high-order numerical schemes such as discontinuous Galerkin finite element methods are preferable to reduce the degrees of freedom, thereby reducing the computational cost. However, when complex geometries emerge, cumbersome refinement is required to approximate the boundary of the computational domain if spatial meshes with straight-sided elements are employed, concealing the advantage of a high-order scheme. In this work, we extend the idea of the non-uniform rational B-splines enhanced finite element method. By embodying accurate geometric information, including parametric descriptions for curved boundaries and sampling information of rough surfaces reconstructed from scanning electron microscope images or by a random growth approach, into the faces of the elements adjacent to the physical boundary, the geometric inaccuracies and heavy refinement can be eliminated in a very coarse mesh. Strategies to define the polynomial basis and compute the integrals over the geometry-embodied elements are investigated. Numerical results, including heat conduction in a silicon ring, nano-porous media with circular pores, and a square domain with a rough boundary, show that to obtain solutions with the same order of accuracy, the discontinuous Galerkin method performed on accurate-geometry-embodied meshes can be 10-100 times faster than that implemented on straight-sided meshes. The efficiency of higher-order discretization methods is fully promoted, where fewer spatial elements combined with higher-order approximating polynomials are preferable to obtain solutions with high accuracy and reduced computational cost.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"313 ","pages":"Article 109623"},"PeriodicalIF":7.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843996","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}

引用次数: 0

pyBoLaNO: A Python symbolic package for normal ordering involving bosonic ladder operators pyBoLaNO：一个Python符号包，用于涉及玻色子阶梯操作符的正常排序

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-15 DOI: 10.1016/j.cpc.2025.109622

Hendry M. Lim , Donny Dwiputra , M. Shoufie Ukhtary , Ahmad R.T. Nugraha

{"title":"pyBoLaNO: A Python symbolic package for normal ordering involving bosonic ladder operators","authors":"Hendry M. Lim , Donny Dwiputra , M. Shoufie Ukhtary , Ahmad R.T. Nugraha","doi":"10.1016/j.cpc.2025.109622","DOIUrl":"10.1016/j.cpc.2025.109622","url":null,"abstract":"<div><div>We present <span>pyBoLaNO</span>, a <span>Python</span> symbolic package based on <span>SymPy</span> to quickly normal-order any polynomial in bosonic ladder operators regarding the canonical commutation relations, using Blasiak's formulae. By extension, this package offers the normal ordering of commutators of any two polynomials in bosonic ladder operators and the evaluation of the normal-ordered expectation value evolution in the Lindblad master equation framework for open quantum systems. The package supports multipartite descriptions and multiprocessing. We describe the package's workflow, show examples of use, and discuss its computational performance. All codes and examples are available on our GitHub repository.</div></div><div><h3>Program summary</h3><div><em>Program title:</em> <span>pyBoLaNO</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/v2jkpvd9z4.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/hendry24/pyBoLaNO</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT License</div><div><em>Programming language:</em> <span>Python</span></div><div><em>Nature of problem:</em> Normal ordering involving bosonic ladder operators regarding the canonical commutation relations.</div><div><em>Solution method:</em> Blasiak's formulae for the normal ordering of an arbitrary monomial in bosonic ladder operators regarding the canonical commutation relations. Symbolic programming is fully provided by <span>SymPy</span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"313 ","pages":"Article 109622"},"PeriodicalIF":7.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837972","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}

引用次数: 0

Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine 基于物理的数据驱动的分布式计算模型：在旋转爆炸火箭发动机上的应用

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-14 DOI: 10.1016/j.cpc.2025.109619

Ionuţ-Gabriel Farcaş , Rayomand P. Gundevia , Ramakanth Munipalli , Karen E. Willcox

{"title":"Distributed computing for physics-based data-driven reduced modeling at scale: Application to a rotating detonation rocket engine","authors":"Ionuţ-Gabriel Farcaş , Rayomand P. Gundevia , Ramakanth Munipalli , Karen E. Willcox","doi":"10.1016/j.cpc.2025.109619","DOIUrl":"10.1016/j.cpc.2025.109619","url":null,"abstract":"<div><div>High-performance computing (HPC) has revolutionized our ability to perform detailed simulations of complex real-world processes. A prominent contemporary example is from aerospace propulsion, where HPC is used for rotating detonation rocket engine (RDRE) simulations in support of the design of next-generation rocket engines; however, these simulations take millions of core hours even on powerful supercomputers, which makes them impractical for engineering tasks like design exploration and risk assessment. Data-driven reduced-order models (ROMs) aim to address this limitation by constructing computationally cheap yet sufficiently accurate approximations that serve as surrogates for the high-fidelity model. This paper contributes a distributed memory algorithm that achieves fast and scalable construction of predictive physics-based ROMs trained from sparse datasets of extremely large state dimension. The algorithm learns structured physics-based ROMs that approximate the dynamical systems underlying those datasets. This enables model reduction for problems at a scale and complexity that exceeds the capabilities of standard, serial approaches. We demonstrate our algorithm's scalability using up to <span><math><mn>2</mn><mo>,</mo><mn>048</mn></math></span> cores on the Frontera supercomputer at the Texas Advanced Computing Center. We focus on a real-world three-dimensional RDRE for which one millisecond of simulated physical time requires one million core hours on a supercomputer. Using a training dataset of <span><math><mn>2</mn><mo>,</mo><mn>536</mn></math></span> snapshots each of state dimension 76 million, our distributed algorithm enables the construction of a predictive data-driven reduced model in just 13 seconds on <span><math><mn>2</mn><mo>,</mo><mn>048</mn></math></span> cores on Frontera.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"313 ","pages":"Article 109619"},"PeriodicalIF":7.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833918","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}

引用次数: 0

Efficient data-driven polarization learning for attosecond science and nonperturbative nonlinear optics 用于阿秒科学和非微扰非线性光学的有效数据驱动偏振学习

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-11 DOI: 10.1016/j.cpc.2025.109617

Emmanuel Lorin , Charlotte Noxon

引用次数: 0

Improving the solver for the Balitsky-Kovchegov evolution equation with Automatic Differentiation 利用自动微分改进巴利茨基-科夫切戈夫演化方程的求解器

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-11 DOI: 10.1016/j.cpc.2025.109616

Florian Cougoulic, Piotr Korcyl, Tomasz Stebel

{"title":"Improving the solver for the Balitsky-Kovchegov evolution equation with Automatic Differentiation","authors":"Florian Cougoulic, Piotr Korcyl, Tomasz Stebel","doi":"10.1016/j.cpc.2025.109616","DOIUrl":"10.1016/j.cpc.2025.109616","url":null,"abstract":"<div><div>The Balitsky-Kovchegov (BK) evolution equation is an equation derived from perturbative Quantum Chromodynamics that allows one to evolve with collision energy the scattering amplitude of a pair of quark and antiquark off a hadron target, called the dipole amplitude. The initial condition, being a non-perturbative object, usually has to be modeled separately. Typically, the model contains several tunable parameters that are determined by fitting to experimental data. In this contribution, we propose an implementation of the BK solver using differentiable programming. Automatic differentiation offers the possibility that the first and second derivatives of the amplitude with respect to the initial condition parameters are automatically calculated at all stages of the simulation. This fact should considerably facilitate and speed up the fitting step. Moreover, in the context of Transverse Momentum Distributions (TMD), we demonstrate that automatic differentiation can be used to obtain the first and second derivatives of the amplitude with respect to the quark-antiquark separation. These derivatives can be used to relate various TMD functions to the dipole amplitude. Our C++ code for the solver, which is available in a public repository <span><span>[1]</span></span>, includes the Balitsky one-loop running coupling prescription and the kinematic constraint. This version of the BK equation is widely used in the small-<em>x</em> evolution framework.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"313 ","pages":"Article 109616"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833917","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}

引用次数: 0

Three-dimensional variable resolution for multi-scale modeling in Smoothed Particle Hydrodynamics 光滑粒子流体动力学多尺度三维变分辨率建模

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-11 DOI: 10.1016/j.cpc.2025.109609

Francesco Ricci , Renato Vacondio , José M. Domínguez , Angelantonio Tafuni

{"title":"Three-dimensional variable resolution for multi-scale modeling in Smoothed Particle Hydrodynamics","authors":"Francesco Ricci , Renato Vacondio , José M. Domínguez , Angelantonio Tafuni","doi":"10.1016/j.cpc.2025.109609","DOIUrl":"10.1016/j.cpc.2025.109609","url":null,"abstract":"<div><div>This study builds on our prior 2D variable-resolution framework for Smoothed Particle Hydrodynamics (SPH) using domain decomposition, extending it to the simulation of three-dimensional flows. We enhance the domain decomposition strategy to enable efficient mass transfer across subdomains with varying resolutions. Key improvements include a refined calculation of Eulerian fluxes at the interfaces between different subdomains, including the free surface, and the use of a first-order consistent approximation of the pressure gradient for a smooth transition of the physical variables across the different resolution zones.</div><div>The model is implemented in the SPH solver DualSPHysics and validated through several 3D test cases, including flow past a sphere, water entry of a wedge, and wave-induced motion of a floating box. Simulation results indicate that our 3D multi-resolution model can capture complex fluid-structure interactions effectively, and it can offer significant computational savings over traditional uniform resolution techniques. Our advancements provide a scalable and efficient solution for simulating a wide range of multi-scale engineering applications, especially those involving fluid-structure interaction.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"313 ","pages":"Article 109609"},"PeriodicalIF":7.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143837971","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}

引用次数: 0

Physics-driven complex relaxation for multi-body systems of SPH method SPH方法下多体系统的物理驱动复弛豫

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-11 DOI: 10.1016/j.cpc.2025.109615

Chenxi Zhao , Yongchuan Yu , Oskar J. Haidn , Xiangyu Hu

引用次数: 0

A block-structured nonuniform meshing technique for reducing the degrees-of-freedom in hybrid particle-in-cell plasma simulations 块结构非均匀网格技术用于降低混合粒子胞内等离子体模拟的自由度

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-11 DOI: 10.1016/j.cpc.2025.109612

M.F. Huq , V.V. Srinivasaragavan , O. Sahni , D. Curreli

引用次数: 0

Solving partial differential equations based on preconditioning-pretraining physics-informed neural network 基于预处理-预训练物理信息神经网络的偏微分方程求解方法

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-10 DOI: 10.1016/j.cpc.2025.109601

Zihan Wang , Ziyue Hu , Mingwei Yang , Yalin Dong , Wenlong Huang , Haijun Ren

{"title":"Solving partial differential equations based on preconditioning-pretraining physics-informed neural network","authors":"Zihan Wang , Ziyue Hu , Mingwei Yang , Yalin Dong , Wenlong Huang , Haijun Ren","doi":"10.1016/j.cpc.2025.109601","DOIUrl":"10.1016/j.cpc.2025.109601","url":null,"abstract":"<div><div>Physics-Informed Neural Network (PINN) is a deep learning framework that has been widely employed to solve spatial-temporal partial differential equations (PDEs) across various fields. However, recent numerical experiments indicate that the vanilla-PINN often struggles with PDEs featuring high-frequency solutions or strong nonlinearity. To enhance PINN's performance, we propose a novel strategy called the Preconditioning-Pretraining Physics-Informed Neural Network (PP-PINN). This approach involves transforming the original task into a new system characterized by low frequency and weak nonlinearity over an extended time scale. The transformed PDEs are then solved using a pretraining approach. Additionally, we introduce a new constraint termed “fixed point”, which is beneficial for scenarios with extremely high frequency or strong nonlinearity. To demonstrate the efficacy of our method, we apply the newly developed strategy to three different equations, achieving improved accuracy and reduced computational costs compared to previous approaches which incorporate the pretraining technique. The effectiveness and interpretability of our PP-PINN are also discussed, emphasizing its advantages in tackling high-frequency solutions and strong nonlinearity, thereby offering insights into its broader applicability in complex mathematical modeling.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"312 ","pages":"Article 109601"},"PeriodicalIF":7.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143826155","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}

引用次数: 0

A conservative constrained clustering-merging algorithm for particle-in-cell codes 单元内粒子码的保守约束聚类合并算法

IF 7.2 2区物理与天体物理

Computer Physics Communications Pub Date : 2025-04-10 DOI: 10.1016/j.cpc.2025.109621

Dong-sheng Cai, Ping-yang Wang

引用次数: 0