Computer Physics Communications最新文献

{"title":"KaiEDJ: A program conducting dynamical mean-field theory and magnetic force theory calculation for correlated magnetic materials","authors":"Hyeong Jun Lee ,&nbsp;Taek Jung Kim ,&nbsp;Hongkee Yoon ,&nbsp;Myung Joon Han","doi":"10.1016/j.cpc.2025.109779","DOIUrl":"10.1016/j.cpc.2025.109779","url":null,"abstract":"<div><div>We report the development of a new code <figure><img></figure> which is designed for the combined analysis of correlated electronic structure and magnetism. First, the program provides the self-consistent dynamical mean-field theory solution with its own exact diagonalization ‘solver’. By means of the easy-to-use controllable interface to the external ‘solvers’, it is also capable of conducting quantum Monte Carlo based dynamical mean-field theory calculations. Second, <figure><img></figure> performs magnetic force response theory to compute magnetic coupling constants. As its input Hamiltonian can be constructed from first-principles density functional theory typically through Wannier-type projections, it can serve as a useful tool for studying correlated magnetic materials. Finally, it provides the internal mode of computing spin wave dispersion based on semi-classical approximation. Benchmark calculation results on several prototypical materials are presented together with the details of usage.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> KaiEDJ</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/3w4rvpyhf9.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/KAIST-ELST/KaiEDJ</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> LGPL</div><div><em>Programming language:</em> Julia</div><div><em>Nature of problem:</em> Theoretical calculation of the correlated electronic structure and the magnetic interaction in real materials.</div><div><em>Solution method:</em> Many-body treatment of the magnetic exchange coupling based on the magnetic force response theory considering dynamical potentials with self-energies.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109779"},"PeriodicalIF":3.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757011","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":"High order interpolation of magnetic fields with vector potential reconstruction for particle simulations","authors":"O. Beznosov,&nbsp;J. Bonilla,&nbsp;X.-Z. Tang,&nbsp;G.A. Wimmer","doi":"10.1016/j.cpc.2025.109770","DOIUrl":"10.1016/j.cpc.2025.109770","url":null,"abstract":"<div><div>We propose a method for interpolating divergence-free continuous magnetic fields via vector potential reconstruction using Hermite interpolation, which ensures high-order continuity for applications requiring adaptive, high-order ordinary differential equation (ODE) integrators, such as the Dormand-Prince method. The method provides <span><math><mi>C</mi><mo>(</mo><mi>m</mi><mo>)</mo></math></span> continuity and achieves high-order accuracy, making it particularly suited for particle trajectory integration and Poincaré section analysis under optimal integration order and timestep adjustments. Through numerical experiments, we demonstrate that the Hermite interpolation method preserves volume and continuity, which are critical for conserving toroidal canonical momentum and magnetic moment in guiding center simulations, especially over long-term trajectory integration. Furthermore, we analyze the impact of insufficient derivative continuity on Runge-Kutta schemes and show how it degrades accuracy at low error tolerances, introducing discontinuity-induced truncation errors. Finally, we demonstrate performant Poincaré section analysis in two relevant settings of field data collocated from finite element meshes.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109770"},"PeriodicalIF":3.4,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780395","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":"Kinetic scrape off layer simulations with semi-Lagrangian discontinuous Galerkin schemes","authors":"Lukas Einkemmer,&nbsp;Alexander Moriggl","doi":"10.1016/j.cpc.2025.109775","DOIUrl":"10.1016/j.cpc.2025.109775","url":null,"abstract":"<div><div>In this paper we propose a semi-Lagrangian discontinuous Galerkin solver for the simulation of the scrape off layer for an electron-ion plasma. We use a time adaptive velocity space to deal with fast particles leaving the computational domain, a block structured mesh to resolve the sharp gradient in the plasma sheath, and limiters to avoid oscillations in the density function. In particular, we propose a limiter that can be computed directly from the information used in the semi-Lagrangian discontinuous Galerkin advection step. This limiter can be directly implemented as part of the GPU kernel of the scheme, which avoids an expensive post processing step. We provide numerical results for a set of benchmark problems and compare different limiting strategies.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109775"},"PeriodicalIF":3.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144757010","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":"GPR_calculator: An on-the-fly surrogate model to accelerate massive nudged elastic band calculations","authors":"Isaac Onyango ,&nbsp;Byungkyun Kang ,&nbsp;Qiang Zhu","doi":"10.1016/j.cpc.2025.109781","DOIUrl":"10.1016/j.cpc.2025.109781","url":null,"abstract":"<div><div>We present <span>GPR_calculator</span>, a package based on Python and C++ programming languages to build an on-the-fly surrogate model using Gaussian Process Regression (GPR) to approximate computationally expensive electronic structure calculations. The key idea is to dynamically train a GPR model during the simulation that can accurately predict energies and forces with uncertainty quantification. When the uncertainty is high, the costly electronic structure calculation is performed to obtain the ground truth data, which is then used to update the GPR model. To illustrate the effectiveness of <span>GPR_calculator</span>, we demonstrate its application in Nudged Elastic Band (NEB) simulations of surface diffusion and reactions, achieving 3-10 times acceleration compared to pure ab initio calculations. The source code is available at <span><span>https://github.com/MaterSim/GPR_calculator</span><svg><path></path></svg></span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> GPR_calculator</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/vyhpdf9fkh.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT [1]</div><div><em>Programming language::</em> Python 3 &amp; C++</div><div><em>Nature of problem:</em> Many atomistic simulations—such as geometry optimization, barrier calculations, molecular dynamics, and equation-of-state simulations—require sampling a large number of atomic configurations in a compact phase space. While Density Functional Theory (DFT) provides good accuracy and relatively scalable performance for systems with fewer than hundreds of atoms, it can become prohibitively expensive for massive simulations. This is particularly evident in energy barrier calculations for surface diffusion or reaction studies, where hundreds or thousands of energy and force evaluations are needed.</div><div><em>Solution method:</em> The <span>GPR_calculator</span> is an On-the-Fly Atomistic Calculator based on Gaussian Process Regression (GPR), designed as an add-on module that can be used with the popular Atomic Simulation Environment (ASE). It is essentially a hybrid approach that consists of: (i) a base calculator to provide ground truth reference energy and forces for the given input structure, and (ii) a surrogate model serving as the less expensive approximation trained on-the-fly. When the uncertainty of the GPR prediction exceeds a user-defined threshold, the base calculator is invoked to obtain accurate results and update the GPR model. This adaptive approach ensures accuracy while significantly reducing computational cost.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div><span><span>https://opensource.org/licenses/MIT</span><svg><path></path></svg></span></div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109781"},"PeriodicalIF":3.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144724336","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":"Constrained control of the radiative transport equation: A novel approach based on the frozen diffusion model","authors":"Hualing Zhong ,&nbsp;Hui Xie ,&nbsp;Shuai Wang ,&nbsp;Heng Yong","doi":"10.1016/j.cpc.2025.109777","DOIUrl":"10.1016/j.cpc.2025.109777","url":null,"abstract":"<div><div>We propose the frozen diffusion model (FDM), which is a new method developed on the framework of diffusion model and can be used to solve the constrained control problem of the radiation transport equation. FDM adopts the strategy of “freezing the known conditions”. During the noise diffusion process, this strategy avoids the interference of noise on known information, thus improving the learning efficiency and control precision. Through several numerical experiments, including spatial single-point flux control, spatial region flux control and ones of multiscale transport, the effectiveness and superiority of our method in different situations are fully demonstrated. It is worth noting that FDM greatly reduces the demand for training data, and provides a practical solution for solving complex radiative transport control problems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109777"},"PeriodicalIF":3.4,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722605","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":"Global constraints preserving Lagrange multiplier approach for the incompressible flow-coupled phase-field vesicle membrane models","authors":"Yanqing He ,&nbsp;Qi Li ,&nbsp;Xiaofeng Yang","doi":"10.1016/j.cpc.2025.109773","DOIUrl":"10.1016/j.cpc.2025.109773","url":null,"abstract":"<div><div>This paper presents a novel model for lipid vesicles that overcomes the limitations of classical phase-field elastic bending models, which utilize penalty terms to approximate volume and surface area conservation. Our approach achieves accurate volume conservation and employs a Lagrange multiplier to ensure precise surface area conservation. For the incompressible flow-coupled system, we introduce two efficient, linear, and energy-stable schemes that integrate the scalar auxiliary variable (SAV) approach with the Lagrange multiplier technique. These schemes maintain the efficiency of the SAV approach for unconstrained gradient flows, requiring only the solution of linear equations with constant coefficients at each time step alongside a negligible-cost nonlinear algebraic system. The proposed schemes guarantee unconditional energy stability while effectively preserving global surface area conservation. Furthermore, the numerical schemes are fully decoupled, linear, unconditionally energy-stable, and exhibit second-order accuracy in time. A significant innovation enabling this full decoupling is the introduction of an ordinary differential equation to address nonlinear coupling terms while satisfying the “zero-energy-contribution” property. Extensive 2D and 3D simulations demonstrate the accuracy and stability of our proposed schemes. The code is published as open-source and can be accessed here: <span><span>https://github.com/HYQ688/CAC_New_Lagrange_method</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109773"},"PeriodicalIF":3.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722604","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 field-aligned gyrokinetic solver based on discontinuous Galerkin in tokamak geometry","authors":"Gahyung Jo ,&nbsp;Janghoon Seo ,&nbsp;Jae-Min Kwon ,&nbsp;Eisung Yoon","doi":"10.1016/j.cpc.2025.109769","DOIUrl":"10.1016/j.cpc.2025.109769","url":null,"abstract":"<div><div>This paper presents the development of a hyperbolic solver for the gyrokinetic equation in tokamak geometry. The discontinuous Galerkin method discretizes the gyrokinetic equation on the field-aligned mesh composed of twisted prism-shaped elements in the tokamak domain. The elements are generated by extending the vertices of unstructured triangular elements on a poloidal plane following the equilibrium magnetic field lines. A sub-triangulation is employed to transfer information between nonconforming meshes, which is inevitable when implementing the field-aligned mesh. The numerical integrations of elements in the field-aligned mesh are performed by transforming the numerical integrations of reference elements in a reference element. We investigate the impact of field-aligned mesh on the numerical interpolation of synthetic plasma fluctuation data generated by a ballooning function. The numerical tests show that the field-aligned mesh can significantly improve computational efficiencies. Additionally, we estimate a sufficient condition for a stable temporal discretization of the hyperbolic solver based on a Runge-Kutta method. The estimation indicates that the field-aligned mesh can allow a notable increase of the time step size for stable simulation. In the numerical experiments, the solver shows good conservations of physical quantities such as mass, kinetic energy, and toroidal canonical angular momentum.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109769"},"PeriodicalIF":7.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703530","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 structure-preserving PINN with embedded periodic boundary layer and adaptively enforced initial conditions for geometric flows","authors":"Meng Li ,&nbsp;You Yang","doi":"10.1016/j.cpc.2025.109762","DOIUrl":"10.1016/j.cpc.2025.109762","url":null,"abstract":"<div><div>Geometric flow models, driven by mechanisms such as surface tension, chemical potential, or curvature energy, are widely applied in fields including materials science, biological membrane dynamics, and image processing. In this paper, we propose a structure-preserving physics-informed neural network (PINN) method that incorporates an embedded periodic boundary layer and adaptively enforced initial conditions. This method, referred to as sp-epai-PINN, is specifically designed to solve a range of representative geometric flow problems, including mean curvature flow, surface diffusion flow and elastic flow. The PINN is a mesh-free approach that is particularly well-suited for solving geometric flow problems, as traditional numerical methods often encounter difficulties related to mesh quality during the evolution process. The embedded periodic boundary layer plays a key role in accurately handling flows involving closed curves, while the adaptively enforced initial conditions significantly enhance the model's capability to address complex curve evolution. Pretraining enables the network to inherit the stability of the initial topology, providing a reliable foundation for the subsequent fine-tuning stage. Furthermore, incorporating energy and/or area constraints into the loss function results in a structure-preserving algorithm that maintains the intrinsic geometric properties of the continuous model. These design elements collectively form the main innovations of the sp-epai-PINN framework, which proves to be highly effective in solving the geometric flows. Numerical experiments were extensively conducted to validate the proposed methods, revealing that sp-epai-PINN consistently surpasses traditional PINN approaches in long-term stability, geometric structure preservation, and convergence efficiency.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109762"},"PeriodicalIF":3.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722606","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":"Geometry-aware framework for deep energy method: An application to structural mechanics with hyperelastic materials","authors":"Thi Nguyen Khoa Nguyen ,&nbsp;Thibault Dairay ,&nbsp;Raphaël Meunier ,&nbsp;Jean Di Stasio ,&nbsp;Christophe Millet ,&nbsp;Mathilde Mougeot","doi":"10.1016/j.cpc.2025.109757","DOIUrl":"10.1016/j.cpc.2025.109757","url":null,"abstract":"<div><div>In this work, we introduce a novel physics-informed framework named the Geometry-Aware Deep Energy Method (GADEM) for solving structural mechanics problems on different geometries. As the weak form of the physical system equation (or the energy-based approach) has demonstrated clear advantages compared to the strong form for solving solid mechanics problems, GADEM employs the weak form and aims to infer the solution on multiple shapes of geometries. Integrating a geometry-aware framework into an energy-based method results in an effective physics-informed deep learning model in terms of accuracy and computational cost. Different ways to represent the geometric information and to encode the geometric latent vectors are investigated in this work. We introduce a loss function of GADEM which is minimized based on the potential energy of all considered geometries. An adaptive learning method is also employed for the sampling of collocation points to enhance the performance of GADEM. We present some applications of GADEM to solve solid mechanics problems, including a loading simulation of a toy tire involving contact mechanics and large deformation hyperelasticity. The numerical results of this work demonstrate the remarkable capability of GADEM to infer the solution on various and new shapes of geometries using only one trained model.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109757"},"PeriodicalIF":3.4,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722609","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":"Orbital cluster-based network modelling","authors":"Antonio Colanera ,&nbsp;Nan Deng ,&nbsp;Matteo Chiatto ,&nbsp;Luigi de Luca ,&nbsp;Bernd R. Noack","doi":"10.1016/j.cpc.2025.109771","DOIUrl":"10.1016/j.cpc.2025.109771","url":null,"abstract":"<div><div>We propose a novel reduced-order framework to describe complex multi-frequency fluid dynamics from time-resolved snapshot data. The starting point is the Cluster-based Network Model (CNM), valued for its fully automatable development and human interpretability. Our key innovation is to model the transitions from cluster to cluster much more accurately by replacing snapshot states with short-term trajectories (“orbits”) over multiple clusters, thus avoiding non-physical diffusion of the probability distributions in the dynamics reconstruction. The proposed orbital CNM (oCNM) employs functional clustering to coarse-grain the short-term trajectories. Specifically, different filtering techniques, resulting in different temporal basis expansions, demonstrate the versatility and capability of the oCNM to adapt to diverse flow phenomena. The oCNM is illustrated on the Stuart-Landau oscillator and its post-transient solution with time-varying parameters to test its ability to capture the amplitude selection mechanism and multi-frequency behaviours. Then, the oCNM is applied to the fluidic pinball across varying flow regimes at different Reynolds numbers, including the periodic, quasi-periodic, and chaotic dynamics. This orbital-focused perspective enhances the understanding of complex temporal behaviours by incorporating high-frequency behaviour into the kinematics of short-time trajectories while modelling the dynamics of the lower frequencies. In analogy to Spectral Proper Orthogonal Decomposition, which marked the transition from spatial-only modes to spatio-temporal ones, this work advances from analysing temporal local states to examining piecewise short-term trajectories or orbits. By merging advanced analytical methods, such as the functional representation of short-time trajectories with CNM, this study paves the way for new approaches to dissect the complex dynamics characterising turbulent systems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109771"},"PeriodicalIF":7.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714090","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}