Computer Physics Communications最新文献

{"title":"Packing3D.jl: An open-source analytical framework for computing packing density and mixing indices using partial spherical volumes","authors":"Freddie J. Barter ,&nbsp;Christopher R.K. Windows-Yule","doi":"10.1016/j.cpc.2025.109863","DOIUrl":"10.1016/j.cpc.2025.109863","url":null,"abstract":"<div><div>Accurate quantification of local packing density and mixing in simulations of particulate systems is essential for many industrial applications. Traditional methods which simply count the number of particle centres within a given volume of space (cell) introduce discontinuities at cell boundaries, leading to unreliable measurements of packing density. We introduce Packing3D.jl, an open-source Julia package providing analytic partial-volume calculations for spheres intersecting Cartesian and cylindrical meshes. We derive closed-form solutions for single, double and triple spherical-cap intersections, plus sphere-cylinder overlaps. We implement efficient mesh-generation routines, principal-cell indexing, and data-splitting functions for time-series analyses. Performance and accuracy were validated against simple cubic and face-centred cubic lattices and via boundary-shift continuity tests. Packing3D.jl converges exactly to theoretical lattice densities, eliminates discontinuities at sub-particle resolution, and scales linearly with particle count. Memory usage remains modest (40 B per particle, 48 B per cell). Packing3D.jl provides researchers with continuous, reproducible volume-fraction fields and robust mixing indices at high performance, facilitating sensitivity analyses and optimisation in granular process engineering.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> Packing3D.jl</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/srdxk6f77w.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/fjbarter/Packing3D.jl</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT</div><div><em>Programming language:</em> <span>Julia</span></div><div><em>Nature of problem:</em> Inaccuracy and discontinuity of packing density calculation by counting centres</div><div><em>Solution method:</em> Derive and implement a fast algorithm for analytically calculating particle volumes intersected by planes, providing a continuous and much more accurate measurement of packing density</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109863"},"PeriodicalIF":3.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095929","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":"Scalable modeling of 3D eddy current problem for magnetic fusion devices","authors":"Chanyoung Lee,&nbsp;Juhyung Kim,&nbsp;Gahyung Jo,&nbsp;Jae-Min Kwon,&nbsp;J.G. Bak,&nbsp;Jeongwon Lee","doi":"10.1016/j.cpc.2025.109864","DOIUrl":"10.1016/j.cpc.2025.109864","url":null,"abstract":"<div><div>A novel three-dimensional time-domain eddy current solver, ERRAHI (Eddy cuRRent Analysis on Hierarchical Inductance), is presented. The solver is built upon a vector potential formulation derived from the electric field integral equation (EFIE), with degrees of freedom systematically identified using a spanning tree technique. Topological holes in the domain give rise to additional degrees of freedom associated with global cycles, and these global cycles are efficiently identified and optimized using a robust algorithm.</div><div>Designed for high-performance computing (HPC) environments, ERRAHI integrates the fast multipole method (FMM) with hierarchical matrix compression. In particular, matrix compression is performed via a randomized embedding scheme with FMM, which efficiently constructs low-rank blocks without assembling or storing the full matrix. This approach achieves empirical scaling of <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>1.5</mn></mrow></msup><mo>)</mo></math></span> and asymptotic scaling of <span><math><mi>O</mi><mo>(</mo><msup><mrow><mi>N</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>)</mo></math></span> for total computation time. Leveraging FMM-based hierarchical compression and solving the resulting system with the generalized minimal residual method (GMRES), the code enables scalable analysis of complex tokamak CAD geometries.</div><div>To enhance locality and compressibility of the system matrix, global cycles are decomposed into local basis functions subject to additional constraints. This decomposition, combined with global cycle optimization, significantly improves the compressibility and structural coherence of the system matrix while maintaining accuracy. The solver has been validated against the TEAM7 benchmark, showing excellent agreement. Furthermore, large-scale simulations of the full KSTAR conductor model successfully reproduce the Rogowski coil and magnetic probe measurements from KSTAR vacuum experiments, demonstrating both the validity and applicability of the method in realistic tokamak scenarios.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109864"},"PeriodicalIF":3.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095936","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":"An automated parallel program of Clean Numerical Simulation for chaotic systems governed by ODEs","authors":"Bo Zhang ,&nbsp;Shijun Liao","doi":"10.1016/j.cpc.2025.109855","DOIUrl":"10.1016/j.cpc.2025.109855","url":null,"abstract":"<div><div>Due to the butterfly-effect, numerical noise in chaotic systems grows exponentially, presenting a significant challenge. This issue can be mitigated through the use of Clean Numerical Simulation (CNS) proposed by Liao in 2009, which can effectively reduce numerical noise to a desired (say, arbitrarily low) level in an interval of time that is long enough for statistics. In this paper, we propose the <span>CNSPy</span>, a novel, highly efficient, self-adaptive CNS implementation to obtain the convergent (i.e. reproducible) numerical simulation of chaotic systems governed by a set of ordinary differential equations (ODEs). This software automates the CNS computational workflow by automatically converting Python-defined ODEs into a parallelized C code, eliminating the need for error-prone manual derivation and coding while ensuring high efficiency in high-performance computing (HPC) environments. The code is free and available at <span><span>https://github.com/sjtu-liao/cnspy</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109855"},"PeriodicalIF":3.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109516","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":"SQUIRREL: An open-source software suite for quantum dynamics calculations on complex geometries with time-dependent electric/magnetic fields","authors":"Simon N. Sandhofer ,&nbsp;Mahmut S. Okyay ,&nbsp;Bryan M. Wong","doi":"10.1016/j.cpc.2025.109861","DOIUrl":"10.1016/j.cpc.2025.109861","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We present a general-purpose, open-source software suite, SQUIRREL (Streamlined Quantum Unified Interface for Researching Real-time Excitations with Light), for propagating the time-dependent Schrödinger equation on complex geometries in the presence of time-dependent electric and/or magnetic fields. To handle large systems that can be executed on a conventional desktop computer, the SQUIRREL software suite uses a suite of efficient propagation methods for various quantum dynamics applications, including a new perturbation-based element-dropping algorithm that improves computational performance with minimal loss of accuracy. We analyze the efficacy of these optimizations for Crank-Nicolson, scaled Taylor series approximation, and split-operator propagation methods and discuss the range of their applicability to a variety of quantum dynamics problems. In addition, we provide several examples of time-dependent dynamics calculations and extensive documentation for generating custom geometries, potentials, and time-propagation approaches. Our numerical benchmarks and results demonstrate the versatility of the SQUIRREL software suite for efficiently calculating quantum dynamics in complex nanoscale geometries, particularly in the presence of time-dependent magnetic fields, which have received less attention in previous quantum dynamics studies.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; SQUIRREL&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/kfvs5s88sj.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; GNU General Public License 3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; MATLAB&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; Additional details on calculations with an anisotropic effective mass, determination of timesteps, propagation timings, and element-dropping timings.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; The SQUIRREL software suite solves the time-dependent Schrödinger equation for electronic states in the presence of time-dependent electric and/or magnetic fields. The code is well-suited for calculating electron dynamics of complex nanostructures in the effective mass approximation, using various propagation and sparse-matrix techniques to reduce the computational complexity of these problems. The program provides a user-friendly interface for testing these optimizations and generating custom geometries and potentials for systems of interest.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; The SQUIRREL software suite uses Crank-Nicolson, split-operator, Padé approximant, and scaled-Taylor-series-based propagation methods to calculate electron dynamics on a finite-element basis. The code includes a novel perturbation-based element-dropping method, which enables sparse representations of the typically dense finite element Hamiltonian matrix. This capability enables a highly efficient and flexible approach for calculating driven dynamics in quantum systems ","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109861"},"PeriodicalIF":3.4,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118690","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":"GTS: A Python toolkit for building Gibbs thermodynamic surface with application to obtain high-pressure melting data","authors":"Xuan Zhao,&nbsp;Kun Yin","doi":"10.1016/j.cpc.2025.109858","DOIUrl":"10.1016/j.cpc.2025.109858","url":null,"abstract":"<div><div>Various methods are commonly applied for data acquisition in the melting process of substances under high pressure. However, throughout the application of these methods, challenges persist, including significant time and computational requirements, as well as issues related to hysteresis effects. We introduce the GTS package, a Python toolkit based on the work of J. W. Gibbs to obtain melting data at high pressures in a geometrical manner. We outline the theory behind constructing the Gibbs thermodynamic surface, which includes regions representing the solid and liquid phases. Several examples are presented to demonstrate program execution and validate accuracy by comparing results with prior studies. GTS is openly accessible on GitHub: <span><span>https://github.com/computation-mineral-physics-group/GTS</span><svg><path></path></svg></span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> GTS</div><div><em>CPC Library link to program files:</em> <span><span><span>https://doi.org/10.17632/wkkkv6twgk.1</span></span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span><span>https://github.com/computation-mineral-physics-group/GTS</span></span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License, version 3</div><div><em>Programming language:</em> Python 3</div><div><em>Nature of problem:</em> A Python toolkit that efficiently obtains high-pressure melting data, including melting points and thermodynamic potentials of materials, by constructing the Gibbs thermodynamic surface using a geometrical method.</div><div><em>Solution method:</em> With the <em>ab initio</em> molecular dynamics (AIMD) simulation data in the NVT (N, number of atoms; V, volume; T, temperature) ensemble and the reference point, GTS consists of two steps: first building the surface, second producing the melting data.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109858"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095930","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":"NepTrain and NepTrainKit: Automated active learning and visualization toolkit for neuroevolution potentials","authors":"Chengbing Chen ,&nbsp;Yutong Li ,&nbsp;Rui Zhao ,&nbsp;Zhoulin Liu ,&nbsp;Zheyong Fan ,&nbsp;Gang Tang ,&nbsp;Zhiyong Wang","doi":"10.1016/j.cpc.2025.109859","DOIUrl":"10.1016/j.cpc.2025.109859","url":null,"abstract":"&lt;div&gt;&lt;div&gt;As a machine-learned potential, the neuroevolution potential (NEP) method features exceptional computational efficiency and has been successfully applied in materials science. Constructing high-quality training datasets is crucial for developing accurate NEP models. However, the preparation and screening of NEP training datasets remain a bottleneck for broader applications due to their time-consuming, labor-intensive, and resource-intensive nature. In this work, we have developed NepTrain and NepTrainKit, which are dedicated to initializing and managing training datasets to generate high-quality training sets while automating NEP model training. NepTrain is an open-source Python package that features a bond length filtering method to effectively identify and remove non-physical structures from molecular dynamics trajectories, thereby ensuring high-quality training datasets. NepTrainKit is a graphical user interface (GUI) software designed specifically for NEP training datasets, providing functionalities for data editing, visualization, and interactive exploration. It integrates key features such as outlier identification, farthest-point sampling, non-physical structure detection, and configuration type selection. The combination of these tools enables users to process datasets more efficiently and conveniently. Using &lt;span&gt;&lt;math&gt;&lt;mi&gt;CsPb&lt;/mi&gt;&lt;msub&gt;&lt;mrow&gt;&lt;mi&gt;I&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;/mrow&gt;&lt;/msub&gt;&lt;/math&gt;&lt;/span&gt; as a case study, we demonstrate the complete workflow for training NEP models with NepTrain and further validate the models through materials property predictions. We believe this toolkit will greatly benefit researchers working with machine learning interatomic potentials.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Program summary&lt;/h3&gt;&lt;div&gt;&lt;em&gt;Program Title:&lt;/em&gt; NepTrain and NepTrainKit&lt;/div&gt;&lt;div&gt;&lt;em&gt;CPC Library link to program files:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://doi.org/10.17632/4s97yg7j9t.1&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Developer's repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/aboys-cb/NepTrain&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt; and &lt;span&gt;&lt;span&gt;https://github.com/aboys-cb/NepTrainKit&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;&lt;/div&gt;&lt;div&gt;&lt;em&gt;Licensing provisions:&lt;/em&gt; GPLv3&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Python&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; The NEP method, a novel machine learning potential model, has demonstrated broad application prospects in materials science due to its excellent computational efficiency. However, the development of accurate NEP models heavily depends on the construction of high-quality training datasets. The preparation and iterative refinement of these datasets largely rely on the researcher's expertise, which poses a significant barrier for beginners attempting to use NEP and similar machine learning potential methods.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; NepTrain is an open-source Python package that features a bond length filtering method to effectively identify and remove non-physic","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109859"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095835","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":"DiracBilinears.jl: A package for computing Dirac bilinears in solids","authors":"Tatsuya Miki ,&nbsp;Hsiao-Yi Chen ,&nbsp;Takashi Koretsune ,&nbsp;Yusuke Nomura","doi":"10.1016/j.cpc.2025.109857","DOIUrl":"10.1016/j.cpc.2025.109857","url":null,"abstract":"<div><div>DiracBilinears.jl is a Julia package for computing Dirac bilinears, which are fundamental physical quantities of electrons in relativistic quantum theory, using first-principles calculations for solids. In relativistic quantum theory, 16 independent bilinears can be defined using the four-component Dirac field. To focus on the low-energy physics typically considered in condensed matter physics, we consider the bilinears represented by the non-relativistic two-component Schrödinger field, obtained from the <span><math><mn>1</mn><mo>/</mo><mi>m</mi></math></span> expansion to leading order. This package can evaluate the spatial distributions and Wannier matrix elements of the Dirac bilinears in solids quantitatively by connecting to the external first-principles calculation packages, including Quantum ESPRESSO, Wannier90, and wan2respack.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> DiracBilinears.jl</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/j57y5cjkmc.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/TatsuyaMiki/DiracBilinears.jl.git</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU General Public License 3.0</div><div><em>Programming language:</em> Julia</div><div><em>External software:</em> <span>Quantum ESPRESSO</span>, <span>Wannier90</span>, <span>wan2respack</span></div><div><em>Nature of problem:</em> In relativistic quantum theory, Dirac bilinears are the fundamental physical quantities derived from the Dirac field. This package is a tool for the evaluation of the bilinears in solids quantitatively.</div><div><em>Solution method:</em> This package evaluates the non-relativistic expression of Dirac bilinears, focusing on the low-energy regime typically discussed in condensed matter physics. It uses results from first-principles calculations performed with <span>Quantum ESPRESSO</span>, <span>Wannier90</span>, and <span>wan2respack</span>. By using the Bloch wave functions and the Wannier functions obtained from these packages, this package computes spatial distributions and Wannier matrix elements of the bilinears.</div><div><em>Additional comments including restrictions and unusual features:</em> This package requires <span>Quantum ESPRESSO</span> calculations using norm-conserving pseudopotentials and supports <span>wan2respack</span> in “spinor” branch on GitHub.<span><span>¹</span></span></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109857"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095931","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":"PARSE: Physical attribute representativity and stationarity evaluator open-source library for 3D images using scalar and vector metrics","authors":"Andrey S. Zubov ,&nbsp;Yan A. Malyavko ,&nbsp;Marina V. Karsanina ,&nbsp;Nikolay D. Kondratyuk ,&nbsp;Kirill M. Gerke","doi":"10.1016/j.cpc.2025.109860","DOIUrl":"10.1016/j.cpc.2025.109860","url":null,"abstract":"<div><div>The concept of representative volume (REV, or RVE in material sciences) is the cornerstone of continuum scale models – one needs to get the volume big enough so that it could be represented with an averaged value (scalar, vector or tensorial, etc.). While REV should be established in all larger scale simulations, this is rarely done in practice despite widespread adoption of 3D imaging devices in all research areas starting from petroleum engineering, material sciences and spanning to biology. Sometimes REV analysis is performed in the form of very simple procedures, such as a check for convergence of porosity or surface area, which is technically identical to omitting it entirely. The main reason for this to happen is the poor understanding of REV concept in general (mainly its connection to spatial stationarity and necessity for vector metrics with high information content) and unavailability of open-source robust solutions. In this work we present <figure><img></figure> library that solves exactly this problem – we developed an easy to use and well-documented code based on rigorous research carried out recently in explaining the “dark sides” of representativity. In addition to REV, our code allows spatial stationarity analysis and comparison of samples (with subsequent clusterization into different groups) based on vector metrics – correlation functions, persistence diagrams and pore-network statistics, that altogether possess high information content which is critical in establishing stationarity and REV. We test our library on images produced by known statistical processes, such as Poisson spheres. After verification, we show how to compare different samples and group them depending on their “structural DNA”. All solutions explained in the paper are represented by Jupiter notebooks that can be used to perform similar analysis, moreover, the class structure of <figure><img></figure> library allows painless modifications to be implemented. We believe that such an open-source library will be useful in numerous fields and will become an invaluable tool for 3D image analysis.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109860"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095932","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":"Ants3 toolkit: Front-end for Geant4 with interactive GUI and Python scripting","authors":"A. Morozov,&nbsp;L.M.S. Margato,&nbsp;G. Canezin,&nbsp;J. Gonzalez","doi":"10.1016/j.cpc.2025.109869","DOIUrl":"10.1016/j.cpc.2025.109869","url":null,"abstract":"<div><div>Ants3 is a toolkit that serves as a front-end for particle simulations in Geant4 and offers a custom simulator for optical photons. It features a fully interactive Graphical User Interface and an extensive scripting system based on general-purpose scripting languages (Python and JavaScript). Ants3 covers the entire detector simulation/optimization cycle, providing an intuitive approach for configuration of the geometry and simulation conditions, the possibility to automatically distribute workload over local and network resources, and giving a suite of versatile tools based on CERN ROOT for the analysis of the results. The intended application area is the development of new detectors and readout methods. The toolkit has been designed to be user-friendly for those with little experience in simulations and programming.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"318 ","pages":"Article 109869"},"PeriodicalIF":3.4,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156856","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":"Tropical sampling from Feynman measures","authors":"Michael Borinsky ,&nbsp;Mathijs Fraaije","doi":"10.1016/j.cpc.2025.109846","DOIUrl":"10.1016/j.cpc.2025.109846","url":null,"abstract":"<div><div>We introduce an algorithm that samples a set of loop momenta distributed as a given Feynman integrand. The algorithm uses the tropical sampling method and can be applied to evaluate phase-space-type integrals efficiently. We provide an implementation, <span>momtrop</span>, and apply it to a series of relevant integrals from the loop-tree duality framework. Compared to naive sampling methods, we observe convergence speedups by factors of more than 10⁶.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>momtrop</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/v9mxr9dw2z.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/alphal00p/momtrop</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> MIT</div><div><em>Programming language:</em> <span>Rust</span></div><div><em>Nature of problem:</em> Efficient numerical evaluation of Feynman-type integrals (e.g. phase space or loop-tree duality integrals).</div><div><em>Solution method:</em> Efficient sampling of loop momenta distributed as the Feynman measure (i.e. the integrand of a scalar Euclidean Feynman integral) using tropical sampling [1]. The input to the library is the graph associated to the Feynman measure. From the graph a sampler is produced that takes as input a set of uniformly distributed random numbers and returns a (weighted) set of loop momenta.</div><div><em>Additional comments including restrictions and unusual features:</em> Memory usage is exponential in the number of propagators of the Feynman integral. There can be numerical instabilities if the parameters are close to a divergent configuration.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>M. Borinsky, Tropical Monte Carlo quadrature for Feynman integrals, Ann. Inst. H. Poincare D Comb. Phys. Interact. 10 (4) (2023) 635–685. <span><span>arXiv:2008.12310</span><svg><path></path></svg></span>, <span><span>https://doi.org/10.4171/aihpd/158</span><svg><path></path></svg></span>.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109846"},"PeriodicalIF":3.4,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145095831","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}