Computer Physics Communications最新文献

{"title":"The qspec Python package: A physics toolbox for laser spectroscopy","authors":"P. Müller ,&nbsp;W. Nörtershäuser","doi":"10.1016/j.cpc.2025.109550","DOIUrl":"10.1016/j.cpc.2025.109550","url":null,"abstract":"<div><div>The analysis of experimental results with Python often requires writing many code scripts which all need access to the same set of functions. In a common field of research, this set will be nearly the same for many users. The <span>qspec</span> Python package was developed to provide functions for physical formulas, simulations and data analysis routines widely used in laser spectroscopy and related fields. Most functions are compatible with <span>numpy</span> arrays, enabling fast calculations with large samples of data. A multidimensional linear regression algorithm enables a King plot analyses over multiple atomic transitions. A modular framework for constructing lineshape models can be used to fit large sets of spectroscopy data. A simulation module within the package provides user-friendly methods to simulate the coherent time-evolution of atoms in electromagnetic fields without the need to explicitly derive a Hamiltonian.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109550"},"PeriodicalIF":7.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529302","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":"eTraj.jl: Trajectory-based simulation for strong-field ionization","authors":"Mingyu Zhu ,&nbsp;Hongcheng Ni ,&nbsp;Jian Wu","doi":"10.1016/j.cpc.2025.109549","DOIUrl":"10.1016/j.cpc.2025.109549","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The dynamics of light-matter interactions in the realm of strong-field ionization has been a focal point and has attracted widespread interest. We present the &lt;span&gt;eTraj.jl&lt;/span&gt; program package, designed to implement established classical/semiclassical trajectory-based methods to determine the photoelectron momentum distribution resulting from strong-field ionization of both atoms and molecules. The program operates within a unified theoretical framework that separates the trajectory-based computation into two stages: initial-condition preparation and trajectory evolution. For initial-condition preparation, we provide several methods, including the Strong-Field Approximation with Saddle-Point Approximation (SFA-SPA), SFA-SPA with Non-adiabatic Expansion (SFA-SPANE), and the Ammosov-Delone-Krainov theory (ADK), with atomic and molecular variants, as well as the Weak-Field Asymptotic Theory (WFAT) for molecules. For trajectory evolution, available options are Classical Trajectory Monte-Carlo (CTMC), which employs purely classical electron trajectories, and the Quantum Trajectory Monte-Carlo (QTMC) and Semi-Classical Two-Step model (SCTS), which include the quantum phase during trajectory evolution. The program is a versatile, efficient, flexible, and out-of-the-box solution for trajectory-based simulations for strong-field ionization. It is designed with user-friendliness in mind and is expected to serve as a valuable and powerful tool for the community of strong-field physics.&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; &lt;span&gt;eTraj.jl&lt;/span&gt;&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/10.17632/33fm297cz4.1&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/TheStarAlight/eTraj.jl&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; Apache-2.0&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language:&lt;/em&gt; Julia&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Atoms and molecules exposed in an intense laser field go through complex processes of ionization through mechanisms such as multi-photon ionization and tunneling ionization. The trajectory-based methods are powerful tools for simulating these processes, and have considerable advantages over the time-dependent Schrödinger equation (TDSE) and the strong-field approximation (SFA). However, the community lacks a unified theoretical framework for trajectory-based methods, and there are no public-available code that implements the schemes.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; We developed a general, efficient, flexible, and out-of-the-box solution for trajectory-based simulation program named after &lt;span&gt;eTraj.jl&lt;/span&gt; using the Julia programming language. This program conducts trajectory-based classical/semiclassical simulations of photoelectron dynamics under the single-active-electron approximation and the Born-Oppenheimer approximation. It supports multiple method","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109549"},"PeriodicalIF":7.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508992","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":"Monte Carlo simulation development and implementation of the GiBUU model for neutrino experiments","authors":"Leonidas Aliaga Soplín,&nbsp;Raquel Castillo Fernández,&nbsp;Jasper Gustafson,&nbsp;Declan Quinn,&nbsp;Shweta Yadav","doi":"10.1016/j.cpc.2025.109553","DOIUrl":"10.1016/j.cpc.2025.109553","url":null,"abstract":"<div><div>This paper introduces a Monte Carlo simulation generated with the GiBUU model for neutrino experiments. The simulation generates realistic neutrino event samples, contributing to the prediction and interpretation of experimental outcomes. The results showcase the performance of the GiBUU-based simulation framework, emphasizing its fidelity to the original GiBUU cross-section model. This first implementation enables future work on developing the infrastructure to propagate systematic uncertainties. These contributions enhance the precision of experimental predictions and provide a platform for further exploration in future studies.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109553"},"PeriodicalIF":7.2,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509005","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":"ToMSGKpoint: A user-friendly package for computing symmetry transformation properties of electronic eigenstates of nonmagnetic and magnetic crystalline materials","authors":"Liangliang Huang ,&nbsp;Xiangang Wan ,&nbsp;Feng Tang","doi":"10.1016/j.cpc.2025.109510","DOIUrl":"10.1016/j.cpc.2025.109510","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The calculation of irreducible (co-)representations of energy bands at high-symmetry points (HSPs) is essential for high-throughput research on topological materials based on symmetry-indicators or topological quantum chemistry. However, existing computational packages usually require transforming crystal structures adapted to specific conventions, thus hindering extensive application, especially to materials whose symmetries are yet to be identified. To address this issue, we developed a Mathematica package, &lt;span&gt;ToMSGKpoint&lt;/span&gt;, capable of determining the little groups and irreducible (co-)representations of little groups of HSPs, high-symmetry lines (HSLs), and high-symmetry planes (HSPLs) for any nonmagnetic and magnetic crystalline materials in two and three dimensions, with or without considering spin-orbit coupling. To the best of our knowledge, this is the first package to achieve such functionality. The package also provides magnetic space group operations, supports the analysis of irreducible (co-)representations of energy bands at HSPs, HSLs, and HSPLs using electronic wavefunctions obtained from &lt;em&gt;ab initio&lt;/em&gt; calculations interfaced with VASP. Designed for user convenience, the package generates results in a few simple steps and presents all relevant information in a clear tabular format. Its versatility is demonstrated through applications to nonmagnetic topological insulator Bi&lt;sub&gt;2&lt;/sub&gt;Se&lt;sub&gt;3&lt;/sub&gt; and Dirac semimetal Na&lt;sub&gt;3&lt;/sub&gt;Bi, as well as the antiferromagnetic topological material MnBi&lt;sub&gt;2&lt;/sub&gt;Te&lt;sub&gt;4&lt;/sub&gt;. Suitable for any crystal structure, this package can be conveniently applied in a streamlined study once magnetic space group varies with various symmetry-breakings caused by phase transitions.&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; &lt;span&gt;ToMSGKpoint&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/FengTang1990/ToMSGKpoint&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; Wolfram&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; The package &lt;span&gt;ToMSGKpoint&lt;/span&gt; provides magnetic space group operations for any crystal structure, along with the little groups of high-symmetry points, lines, and planes, and their corresponding irreducible (co-)representations. It also facilitates the transformation from a customized crystal structure to the Bradley-Cracknell convention. Furthermore, based on electronic wavefunctions obtained from VASP calculations, the package computes the irreducible (co-)representations of energy bands at high-symmetry points, lines, and planes.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; In order to calculate the irreducible (co-)representations of the little groups at high-symmetry points, lines, and planes, we first obtain the transformation from the customized crystal structure convention to the Bradley-Cracknell convention. Using this transformation","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109510"},"PeriodicalIF":7.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453887","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":"Galactic distribution of supernovae and OB associations","authors":"M. Kachelrieß,&nbsp;V. Mikalsen","doi":"10.1016/j.cpc.2025.109537","DOIUrl":"10.1016/j.cpc.2025.109537","url":null,"abstract":"<div><div>We update and extend a previous model by Higdon and Lingenfelter for the longitudinal profile of the N<!--> <!-->II intensity in the Galactic plane. The model is based on four logarithmic spiral arms, to which features like the Local Arm and local sources are added. Connecting then the N<!--> <!-->II to the H<!--> <!-->II emission, we use this model to determine the average spatial distribution of OB associations in the Milky Way. Combined with a stellar mass and cluster distribution function, the model predicts the average spatial and temporal distribution of core-collapse supernovae in the Milky Way. In addition to this average population, we account for supernovae from observed OB associations, providing thereby a more accurate description of the nearby Galaxy. The complete model is made publicly available in the python code <span>SNOB</span>.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>SNOB<!--> <!-->1.1</span>: Simulating the distribution of SuperNovae and OB associations in the Milky Way.</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/hz5vbsvy7d.1</span><svg><path></path></svg></span>.</div><div><em>Licensing provisions:</em> CC by NC 3.0.</div><div><em>Programming language:</em> Python 3.8</div><div><em>Nature of problem:</em> Determination of the distribution of OB associations from the observed N<!--> <!-->II line intensity; derivation of the resulting distribution of core-collapse supernovae.</div><div><em>Solution method:</em> Numerical integration of line-of-sight integrals for the N<!--> <!-->II line intensity; Monte Carlo simulation of the spatial and time distribution of OB associations and core-collapse supernovae in the Milky Way.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109537"},"PeriodicalIF":7.2,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437681","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":"curvedSpaceSim: A framework for simulating particles interacting along geodesics","authors":"Toler H. Webb,&nbsp;Daniel M. Sussman","doi":"10.1016/j.cpc.2025.109545","DOIUrl":"10.1016/j.cpc.2025.109545","url":null,"abstract":"<div><div>A large number of powerful, high-quality, and open-source simulation packages exist to efficiently perform molecular dynamics simulations, and their prevalence has greatly accelerated discoveries across a wide range of scientific domains. These packages typically simulate particles in flat (Euclidean) space, with options to specify a variety of boundary conditions. While more exotic, many physical systems are constrained to and interact across curved surfaces, such as organisms moving across the landscape, colloids pinned at curved fluid-fluid interfaces, and layers of epithelial cells forming highly curved tissues. The calculation of distances and the updating of equations of motion in idealized geometries (namely, on surfaces of constant curvature) can be done analytically, but it is much more challenging to efficiently perform molecular-dynamics-like simulations on arbitrarily curved surfaces. This article discusses a simulation framework which combines tools from particle-based simulations with recent work in discrete differential geometry to model particles that interact via geodesic distances and move on an arbitrarily curved surface. We present computational cost estimates for a variety of surface complexities with and without various algorithmic specializations (e.g., restrictions to short-range interaction potentials, or multi-threaded parallelization). Our flexible and extensible framework is set up to easily handle both equilibrium and non-equilibrium dynamics, and will enable researchers to access time- and particle-number-scales previously inaccessible.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> curvedSpaceSim</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/wc7nxf93ym.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/sussmanLab/curvedSpaceSim</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> C<strong>++</strong></div><div><em>Nature of problem:</em> Molecular-dynamics-like simulations of degrees of freedom evolving on a curved two-dimensional manifold according to standard equilibrium or non-equilibrium equations of motion and interacting via geodesics.</div><div><em>Solution method:</em> We discretize both time and space, using modern tools from discrete differential geometry to efficiently find geodesic paths and distances. MPI parallelization is implemented to access large system sizes, and where appropriate (e.g., when dealing with short-ranged inter-particle potentials) we implement the ability to aggressively prune data structures, greatly decreasing the computational cost of our many-particle simulations.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109545"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428087","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":"Deep learning-enhanced reduced-order ensemble Kalman filter for efficient Bayesian data assimilation of parametric PDEs","authors":"Yanyan Wang ,&nbsp;Liang Yan ,&nbsp;Tao Zhou","doi":"10.1016/j.cpc.2025.109544","DOIUrl":"10.1016/j.cpc.2025.109544","url":null,"abstract":"<div><div>Bayesian data assimilation for systems governed by parametric partial differential equations (PDEs) is computationally demanding due to the need for multiple forward model evaluations. Reduced-order models (ROMs) have been widely used to reduce the computational burden. However, traditional ROM techniques rely on linear mode superposition, which frequently fails to capture nonlinear time-dependent dynamics efficiently and leads to biases in the assimilation results. To address these limitations, we introduce a new deep learning-enhanced reduced-order ensemble Kalman filter (DR-EnKF) method for Bayesian data assimilation. The proposed approach employs a two-tiered learning framework. First, the full-order model is reduced using operator inference, which finds the primary dynamics of the system through long-term simulations generated from coarse-grid data. Second, a model error network is trained with short-term simulation data from a fine grid to learn about the ROM-induced discrepancy. The learned network is then used online to correct the ROM-based EnKF, resulting in more accurate state updates during the assimilation process. The performance of the proposed method is evaluated on several benchmark problems, including the Burgers' equation, the FitzHugh-Nagumo model, and advection-diffusion-reaction systems. The results show considerable computational speedup without compromising accuracy, making this approach an effective tool for large-scale data assimilation tasks.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109544"},"PeriodicalIF":7.2,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464046","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":"JAX-based aeroelastic simulation engine for differentiable aircraft dynamics","authors":"Alvaro Cea,&nbsp;Rafael Palacios","doi":"10.1016/j.cpc.2025.109547","DOIUrl":"10.1016/j.cpc.2025.109547","url":null,"abstract":"&lt;div&gt;&lt;div&gt;A novel methodology is presented in this paper for the structural and aeroelastic analysis of large flexible systems with slender, streamlined components, such as aircraft or wind turbines. Leveraging on the numerical library JAX, a nonlinear formulation based on velocities and strains enables a highly vectorised codebase that is especially suitable for the integration of aerodynamic loads which naturally appear as follower forces. In addition to that, JAX automatic differentiation capabilities are used to obtain gradients that allow the solver to be embedded into broader multidisciplinary optimization frameworks. The general solution starts from a linear Finite-Element (FE) model of arbitrary complexity, on which a structural model order reduction is performed. A nonlinear description of the reduced model follows, with the corresponding reconstruction of the full 3D dynamics. It is shown to be highly accurate and efficient on representative aircraft models are shown. An extensive verification has been carried out by comparison with MSC Nastran full-FE linear and nonlinear solutions. Furthermore the nonlinear gust response of a full aircraft configuration with over half a million degrees-of-freedom is computed, and it is faster than its frequency-based, linear equivalent as implemented by a commercial package. Therefore this could be harnessed by aircraft loads engineers to add geometrically nonlinear effects to their existing workflows at no extra computational effort. Finally, automatic differentiation on both static and dynamic problems is validated against finite-differences, which combined with a near real-time performance of the solvers opens new possibilities for aeroelastic studies and design optimization.&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; FENIAX&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/wxy56w8j6y.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/ACea15/FENIAX&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;https://github.com/ACea15/FENIAX/tree/master/docs/reports/CPC24&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 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; Aeroelastic solutions that couple structural and fluid domains are paramount in the study of many engineering structures such aeroplanes, bridges or wind-turbines. They often feature slender and light components that can potentially undergo large deflections that require of geometrically nonlinear modelling tools, which are linked to higher computational resources and potentially prohibitively simulation times. Moreover, since the advent of computers, organizations have gathered an expertise to build large finite-element-based aeroelastic models based on linear formulations that might not be easily amendable for nonlinear analysis. We pr","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"311 ","pages":"Article 109547"},"PeriodicalIF":7.2,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437728","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":"CaLES: A GPU-accelerated solver for large-eddy simulation of wall-bounded flows","authors":"Maochao Xiao ,&nbsp;Alessandro Ceci ,&nbsp;Pedro Costa ,&nbsp;Johan Larsson ,&nbsp;Sergio Pirozzoli","doi":"10.1016/j.cpc.2025.109546","DOIUrl":"10.1016/j.cpc.2025.109546","url":null,"abstract":"<div><div>We introduce CaLES, a GPU-accelerated finite-difference solver designed for large-eddy simulations (LES) of incompressible wall-bounded flows in massively parallel environments. Built upon the existing direct numerical simulation (DNS) solver CaNS, CaLES relies on low-storage, third-order Runge-Kutta schemes for temporal discretization, with the option to treat viscous terms via an implicit Crank-Nicolson scheme in one or three directions. A fast direct solver, based on eigenfunction expansions, is used to solve the discretized Poisson/Helmholtz equations. For turbulence modeling, the classical Smagorinsky model with van Driest near-wall damping and the dynamic Smagorinsky model are implemented, along with a logarithmic law wall model. GPU acceleration is achieved through OpenACC directives, following CaNS-2.3.0. Performance assessments were conducted on the Leonardo cluster at CINECA, Italy. Each node is equipped with one Intel Xeon Platinum 8358 CPU (2.60 GHz, 32 cores) and four NVIDIA A100 GPUs (64 GB HBM2e), interconnected via NVLink 3.0 (200 GB/s). The inter-node communication bandwidth is 25 GB/s, supported by a DragonFly+ network architecture with NVIDIA Mellanox InfiniBand HDR. Results indicate that the computational speed on a single GPU is equivalent to approximately 15 CPU nodes, depending on the treatment of viscous terms and the subgrid-scale model, and that the solver efficiently scales across multiple GPUs. The predictive capability of CaLES has been tested using multiple flow cases, including decaying isotropic turbulence, turbulent channel flow, and turbulent duct flow. The high computational efficiency of the solver enables grid convergence studies on extremely fine grids, pinpointing non-monotonic grid convergence for wall-modeled LES.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109546"},"PeriodicalIF":7.2,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427574","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":"Enhancing precision in J/ψ mass estimation: A study of ensemble and deep learning methods","authors":"Serpil Yalcin Kuzu ,&nbsp;Ayben Karasu Uysal ,&nbsp;Mustafa Kaya","doi":"10.1016/j.cpc.2025.109534","DOIUrl":"10.1016/j.cpc.2025.109534","url":null,"abstract":"<div><div>This study evaluates ensemble learning methods and Deep Neural Networks (DNNs) for identifying <em>J/</em><span><math><mi>ψ</mi><mo>→</mo><msup><mrow><mi>μ</mi></mrow><mrow><mo>+</mo></mrow></msup><msup><mrow><mi>μ</mi></mrow><mrow><mo>−</mo></mrow></msup></math></span> events in proton-proton collisions at the LHC, focusing on the dimuon decay channel within a skewed dataset. For this purpose, 8 different machine learning models based on Random Forest (RF), Gradient Boosting Decision Trees (GBDT), and DNNs were implemented to investigate the most effective approach for charmonium event determination. Performance metrics such as precision, recall, F-1 Score, geometric mean (G-mean), and balanced accuracy (BAcc) are employed, with StratifiedKFold cross-validation verifying the models' robustness in skewed data scenarios. Results demonstrate DNNs as the most proficient, underscoring their potential in complex data analysis in particle physics. Utilizing the Crystal Ball (CB) function on the results of DNNs, the precision of the <em>J/ψ</em> mass was estimated. This study not only enhances understanding of machine learning applications in high-energy particle collisions but also sets the stage for more advanced research in this field.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"310 ","pages":"Article 109534"},"PeriodicalIF":7.2,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419691","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}