Computer Physics Communications最新文献

{"title":"DiPolMol-Py: A Python package for calculations for 2Σ ground-state molecules","authors":"Bethan Humphreys,&nbsp;Alex J. Matthies,&nbsp;Hannah J. Williams","doi":"10.1016/j.cpc.2025.109813","DOIUrl":"10.1016/j.cpc.2025.109813","url":null,"abstract":"<div><div>We present the python package DiPolMol-Py, which can be used to calculate the rotational and hyperfine structure of <span><math><mmultiscripts><mrow><mi>Σ</mi></mrow><mprescripts></mprescripts><none></none><mrow><mn>2</mn></mrow></mmultiscripts></math></span> molecules. The calculations can be performed in the presence of dc magnetic fields, dc electric fields and far off-resonant optical fields. We additionally include functions to calculate the polarisability of the molecule and the transition dipole moment between different energy eigenstates. The package is applicable to many of the molecules which can be laser cooled, specifically the alkaline earth fluorides. We provide a constants file which includes many of the required literature values for ⁴⁰CaF, ⁸⁸SrF and ¹³⁸BaF. Additional species can easily be added by updating this file.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> DiPolMol-Py</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/36gp2kd4jj.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/durham-qlm/DiPolMol</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> BSD 3-clause</div><div><em>Programming language:</em> Python ≥3.11</div><div><em>Nature of problem:</em> Calculating the rotational and hyperfine structure for <span><math><mmultiscripts><mrow><mi>Σ</mi></mrow><mprescripts></mprescripts><none></none><mrow><mn>2</mn></mrow></mmultiscripts></math></span> ground state molecules both field free and in the presence of dc magnetic, electric and off-resonant light fields.</div><div><em>Solution method:</em> A Python package which calculates the eigenenergies and eigenvalues via diagonalization of the Hamiltonian.</div><div><em>Additional comments including restrictions and unusual features:</em> This package is based on previous work for <span><math><mmultiscripts><mrow><mi>Σ</mi></mrow><mprescripts></mprescripts><none></none><mrow><mn>1</mn></mrow></mmultiscripts></math></span> molecules [1]. External magnetic and electric fields must be coaxial.</div></div><div><h3>References</h3><div><ul><li><span>[1]</span><span><div>J.A. Blackmore, P.D. Gregory, J.M. Hutson, S.L. Cornish, Comput. Phys. Commun. 282 (2023) 108512, <span><span>https://doi.org/10.1016/j.cpc.2022.108512</span><svg><path></path></svg></span>.</div></span></li></ul></div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109813"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867184","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":"Efficient cosmic ray generator for particle detector simulations","authors":"David Díez-Ibáñez,&nbsp;Luis Obis","doi":"10.1016/j.cpc.2025.109805","DOIUrl":"10.1016/j.cpc.2025.109805","url":null,"abstract":"<div><div>Traditional cosmic ray simulations commonly employ the Monte Carlo method to randomize the energy and direction of each simulated particle, often employing simplified or uncorrelated distributions. The flux of cosmic rays is modelled as incident particles originating from a plane above the object of interest (e.g., detectors in particle physics or surfaces in dosimetry studies) with experimentally determined angular and energy distributions. This strategy is highly inefficient because a significant number of particles never intersect the detector. This paper proposes a refined Monte Carlo method to generate a sample of events that intersect the target volume, ensuring their angular distribution matches that of the conventional approach. It is based on the projection of a sphere containing the target volume onto a plane tangent to it at a fixed angle; this is termed the <em>Probability Distribution Projection</em> (PDP) method. This configuration allows computation of the probability that a cosmic particle hits the sphere at this incoming angle, with this probability being proportional to the area of the corresponding section of a cylinder. The performance of this method demonstrates enhanced computational speed while yielding identical physical results. It has been implemented within the REST-for-Physics framework and tested using the geometry of a real detector, the IAXO-D0 Micromegas X-ray detector for the future axion helioscope BabyIAXO. The proposed method achieves a 37-fold improvement in efficiency compared to the traditional Monte Carlo scheme for the same accuracy, and is particularly advantageous when the target volume deviates from a spherical shape.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"317 ","pages":"Article 109805"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144895991","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":"PrecisionLauricella: Package for numerical computation of Lauricella functions depending on a parameter","authors":"M.A. Bezuglov ,&nbsp;B.A. Kniehl ,&nbsp;A.I. Onishchenko ,&nbsp;O.L. Veretin","doi":"10.1016/j.cpc.2025.109812","DOIUrl":"10.1016/j.cpc.2025.109812","url":null,"abstract":"&lt;div&gt;&lt;div&gt;We introduce the &lt;span&gt;PrecisionLauricella&lt;/span&gt; package, a computational tool developed in Wolfram Mathematica for high-precision numerical evaluations of the Laurent expansion coefficients of Lauricella functions whose parameters depend linearly on a small regulator, &lt;em&gt;ε&lt;/em&gt;. In practical multi-loop calculations, Lauricella functions are required only as series around &lt;span&gt;&lt;math&gt;&lt;mi&gt;ε&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;/math&gt;&lt;/span&gt;, and &lt;span&gt;PrecisionLauricella&lt;/span&gt; is designed specifically to deliver such coefficients with arbitrary precision. The package leverages a method based on analytic continuation via Frobenius generalized power series, providing an efficient and accurate alternative to conventional approaches relying on multi-dimensional series expansions or Mellin–Barnes representations. This one-dimensional approach is particularly advantageous for high-precision calculations and facilitates further optimization through &lt;em&gt;ε&lt;/em&gt;-dependent reconstruction from evaluations at specific numerical values, enabling efficient parallelization. The underlying mathematical framework for this method has been detailed in our previous work, while the current paper focuses on the design, implementation, and practical applications of the &lt;span&gt;PrecisionLauricella&lt;/span&gt; package.&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; PrecisionLauricella&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/10.17632/6f958yz2dr.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://bitbucket.org/BezuglovMaxim/precisionlauricella-package/src/main/&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 Mathematica&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; PrecisionLauricella_Examples.nb&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; Lauricella functions, generalizations of hypergeometric functions, appearing in physics and mathematics, including Feynman integrals and string theory. When their indices depend linearly on a small parameter &lt;em&gt;ε&lt;/em&gt;, their numerical evaluation becomes challenging due to the complexity of high-dimensional series and singularities. Traditional methods, like hypergeometric re-expansion or Mellin–Barnes integrals, often lack efficiency and precision.&lt;/div&gt;&lt;div&gt;Managing multi-dimensional sums exacerbates computational costs, especially for high-precision requirements, making these approaches unsuitable for many practical applications. Thus, there is a pressing need for efficient, scalable methods capable of maintaining numerical accuracy and effectively handling parameter dependencies.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method:&lt;/em&gt; Our method uses the Frobenius approach to achieve analytic continuations of Lauricella functions through generalized power series. Representing the functions as one-dimensional series simplifies high-precision numerical evaluations compa","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109812"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830691","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":"Classical bounds on two-outcome bipartite Bell expressions and linear prepare-and-measure witnesses: Efficient computation in parallel environments such as graphics processing units","authors":"István Márton ,&nbsp;Erika Bene ,&nbsp;Péter Diviánszky ,&nbsp;Gábor Drótos","doi":"10.1016/j.cpc.2025.109809","DOIUrl":"10.1016/j.cpc.2025.109809","url":null,"abstract":"<div><div>The presented program aims at speeding up the brute force computation of the so-called <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>d</mi></mrow></msub></math></span> norm of a matrix <em>M</em> using graphics processing units (GPUs). Alternatives for CPUs have also been implemented, and the algorithm is applicable to any parallel environment. The <span><math><mi>n</mi><mo>×</mo><mi>m</mi></math></span> matrix <em>M</em> has real elements which may represent coefficients of a bipartite Bell expression or those of a linear prepare-and-measure (PM) witness. In this interpretation, the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></math></span> norm is the local bound of the given correlation-type Bell expression, and the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>d</mi></mrow></msub></math></span> norm for <span><math><mi>d</mi><mo>≥</mo><mn>2</mn></math></span> is the classical <em>d</em>-dimensional bound of the given PM witness, which is associated with the communication of <em>d</em>-level classical messages in the PM scenario. The program is also capable of calculating the local bound of Bell expressions including marginals. In all scenarios, the output is assumed to be binary.</div><div>The code for GPUs is written in CUDA C and can utilize one NVIDIA GPU in a computer. To illustrate the performance of our implementation, we refer to Brierley et al. <span><span>[1]</span></span> who needed approximately three weeks to compute the local bound on a Bell expression defined by a <span><math><mn>42</mn><mo>×</mo><mn>42</mn></math></span> matrix on a standard desktop using a single CPU core. In contrast, our efficient implementation of the brute force algorithm allows us to reduce this to three minutes using a single NVIDIA RTX 6000 Ada graphics card on a workstation. For CPUs, the algorithm was implemented with OpenMP and MPI according to the shared and distributed memory models, respectively, and achieves a comparable speedup at a number of CPU cores around 100.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> L_CUDA.cu, L_MPI.c, L_OpenMP.c</div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/scfjjt9svm.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/istvanmarton/L-norms_BruteForce</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GPLv3</div><div><em>Programming language:</em> C, CUDA, OpenMP, MPI</div><div><em>Nature of problem:</em> The computational demand of determining the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>d</mi></mrow></msub></math></span> norm of a matrix of real coefficients is high; exact <span><math><msub><mrow><mi>L</mi></mrow><mrow><mi>d</mi></mrow></msub></math></span> norms have been computed so far for relatively small matrices only. Besides that any exact algorithm appears to scale exponentially with the numbe","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109809"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830769","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":"Adaptive mesh refinement in semi-implicit particle-in-cell method","authors":"Talha Arshad,&nbsp;Yuxi Chen,&nbsp;Gábor Tóth","doi":"10.1016/j.cpc.2025.109806","DOIUrl":"10.1016/j.cpc.2025.109806","url":null,"abstract":"<div><div>The particle-in-cell (PIC) method is powerful for simulating plasma kinetic processes. However, PIC simulations are usually computationally expensive, and improving the computational efficiency is essential for expanding their capabilities. Adaptive mesh refinement (AMR) is an important technique that can be applied to accelerate PIC simulations. In this paper, we introduce a novel adaptive mesh refinement (AMR) algorithm that is implemented for a semi-implicit electromagnetic particle-in-cell (PIC) code. Our approach supports different refinement ratios as well as multiple refinement levels. The electric field solver is carefully designed to minimize artifacts at interfaces of different levels, and we introduce an algorithm to reduce errors in Gauss's law across all levels. To maintain a uniform particle distribution, which is crucial for achieving high computational efficiency, particle splitting and merging techniques are integrated into the code. We validate our algorithm with several tests, including a two-dimensional double current sheet reconnection test, that show accurate solutions on the AMR grid with considerable speed-up relative to a uniform high-resolution grid.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109806"},"PeriodicalIF":3.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830689","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 pencil-distributed finite-difference solver for extreme-scale calculations of turbulent wall flows at high Reynolds number","authors":"Rafael Diez Sanhueza,&nbsp;Jurriaan Peeters,&nbsp;Pedro Costa","doi":"10.1016/j.cpc.2025.109811","DOIUrl":"10.1016/j.cpc.2025.109811","url":null,"abstract":"<div><div>We present a computational method for extreme-scale simulations of incompressible turbulent wall flows at high Reynolds numbers. The numerical algorithm extends a popular method for solving second-order finite differences Poisson/Helmholtz equations using a pencil-distributed parallel tridiagonal solver to improve computational performance at scale. The benefits of this approach were investigated for high-Reynolds-number turbulent channel flow simulations, with up to about 80 billion grid points and 1024 GPUs on the European flagship supercomputers Leonardo and LUMI. An additional GPU porting effort of the entire solver had to be undertaken for the latter. Our results confirm that, while 1D domain decompositions are favorable for smaller systems, they become inefficient or even impossible at large scales. This restriction is relaxed by adopting a pencil-distributed approach. The results show that, at scale, the revised Poisson solver is about twice as fast as the baseline approach with the full-transpose algorithm for 2D domain decompositions. Strong and weak scalability tests show that the performance gains are due to the lower communication footprint. Additionally, to secure high performance when solving for wall-normal implicit diffusion, we propose a reworked flavor of parallel cyclic reduction (PCR) that is split into pre-processing and runtime steps. During pre-processing, small sub-arrays with independent 1D coefficients are computed by parallel GPU threads, without any global GPU communication. Then, at runtime, the reworked PCR enables a fast solution of implicit 1D diffusion without computational overhead. Our results show that the entire numerical solver, coupled with the PCR algorithm, enables extreme-scale simulations with 2D pencil decompositions, which do not suffer performance losses even when compared to the best 1D slab configurations available for smaller systems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109811"},"PeriodicalIF":3.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830688","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":"SIREN: An open-source neutrino injection toolkit","authors":"Austin Schneider ,&nbsp;Nicholas W. Kamp ,&nbsp;Alex Y. Wen","doi":"10.1016/j.cpc.2025.109799","DOIUrl":"10.1016/j.cpc.2025.109799","url":null,"abstract":"<div><div>Modeling of rare neutrino processes often relies on either simple approximations or expensive detector simulations. The former is often not sufficient for interactions with complex morphologies, while the latter is too time-intensive for phenomenological studies. We present <span>SIREN</span> (Sampling and Injection for Rare EveNts), a new tool for neutrino phenomenology and experimental searches alike that enables accurate interaction and detector geometry modeling without the overhead of detailed detector response simulations. <span>SIREN</span> handles the injection of rare process final states and the associated weighting calculations with the speed needed for phenomenological investigations and the detail necessary for dedicated experimental searches. The extensible design of <span>SIREN</span> allows it to support a wide range of experimental designs and Beyond Standard Model neutrino interactions. Users need only specify the physical process, detector geometry, and initial neutrino flux under consideration before they can accurately simulate a model in their detector of choice. We demonstrate the capability of <span>SIREN</span> through two examples: (1) Standard Model <span><math><msub><mrow><mi>ν</mi></mrow><mrow><mi>μ</mi></mrow></msub></math></span> deep inelastic scattering in IceCube, DUNE, and ATLAS; and (2) heavy neutral lepton interactions in MiniBooNE, MINER<em>ν</em>A, and CCM. A variety of detector geometry descriptions, interaction cross sections, and neutrino fluxes are also provided for users to get started with immediately.</div></div><div><h3>Program summary</h3><div><em>Program Title:</em> <span>SIREN</span></div><div><em>CPC Library link to program files:</em> <span><span>https://doi.org/10.17632/j8mftngm5m.1</span><svg><path></path></svg></span></div><div><em>Developer's repository link:</em> <span><span>https://github.com/Harvard-Neutrino/SIREN</span><svg><path></path></svg></span></div><div><em>Licensing provisions:</em> GNU Lesser General Public License, v3</div><div><em>Programming Language:</em> <span>C++</span>17, <span>Python</span></div><div><em>External Routines:</em> <span><span>Boost</span><svg><path></path></svg></span>, <span><span>HDF5</span><svg><path></path></svg></span>, <span><span>pybind11</span><svg><path></path></svg></span>, <span><span>Photospline</span><svg><path></path></svg></span>, <span><span>SuiteSparse</span><svg><path></path></svg></span>, <span><span>DarkNews</span><svg><path></path></svg></span></div><div><em>Nature of problem:</em> Injection and reweighting of neutrinos and rare-processes across diverse experiments and models.</div><div><em>Solution method:</em> An extensible framework for injection and weighting of rare processes with detailed material and geometry modeling and built-in support for a variety of experiments and models.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109799"},"PeriodicalIF":3.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867185","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":"Enhanced partial donor cell method for hyperbolic equations in orthogonal curvilinear coordinates","authors":"Hongyang Luo ,&nbsp;Binzheng Zhang ,&nbsp;John G. Lyon ,&nbsp;Jiaxing Tian","doi":"10.1016/j.cpc.2025.109808","DOIUrl":"10.1016/j.cpc.2025.109808","url":null,"abstract":"<div><div>An enhanced high-order reconstruction method for finite-volume solvers in orthogonal curvilinear coordinates is presented. Extending the classical Partial Donor Method (PDM) to account for geometric effects, the scheme achieves arbitrary high-order convergence while preserving monotonicity and minimizing numerical diffusion. Optimal seventh-order interpolation formulas for uniform cylindrical and spherical-radial grids are derived, complemented by an optional non-clipping algorithm that enhances accuracy near narrow extrema. Extensive tests in both linear and non-linear systems validate the method's high spatial accuracy and non-oscillatory performance. Its straightforward derivation and modest computational overhead render the approach a promising tool for astrophysical, space, and planetary applications, with the potential for extension to other curvilinear systems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109808"},"PeriodicalIF":3.4,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858181","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":"Solving multiscale dynamical systems by deep learning","authors":"Junjie Yao ,&nbsp;Yuxiao Yi ,&nbsp;Liangkai Hang ,&nbsp;Weinan E ,&nbsp;Weizong Wang ,&nbsp;Yaoyu Zhang ,&nbsp;Tianhan Zhang ,&nbsp;Zhi-Qin John Xu","doi":"10.1016/j.cpc.2025.109802","DOIUrl":"10.1016/j.cpc.2025.109802","url":null,"abstract":"<div><div>Multiscale dynamical systems, modeled by high-dimensional stiff ordinary differential equations (ODEs) with wide-ranging characteristic timescales, arise across diverse fields of science and engineering, but their numerical solvers often encounter severe efficiency bottlenecks. This paper introduces a novel <strong>DeePODE</strong> method, which consists of an Evolutionary Monte Carlo Sampling method (EMCS) and an efficient end-to-end deep neural network (DNN) to predict multiscale dynamical systems. The method's primary contribution is its approach to the “curse of dimensionality”– the exponential increase in data requirements as dimensions increase. By integrating Monte Carlo sampling with the system's inherent evolutionary dynamics, DeePODE efficiently generates high-dimensional time-series data covering trajectories with wide characteristic timescales or frequency spectra in the phase space. Appropriate coverage on the frequency spectrum of the training data proves critical for data-driven time-series prediction ability, as neural networks exhibit an intrinsic learning pattern of progressively capturing features from low to high frequencies. We validate this finding across dynamical systems from ecological systems to reactive flows, including a predator-prey model, a power system oscillation, a battery electrolyte thermal runaway, and turbulent reaction-diffusion systems with complex chemical kinetics. The method demonstrates robust generalization capabilities, allowing pre-trained DNN models to accurately predict the behavior in previously unseen scenarios, largely due to the delicately constructed dataset. While theoretical guarantees remain to be established, empirical evidence shows that DeePODE achieves the accuracy of implicit numerical schemes while maintaining the computational efficiency of explicit schemes. This work underscores the crucial relationship between training data distribution and neural network generalization performance. This work demonstrates the potential of deep learning approaches in modeling complex dynamical systems across scientific and engineering domains.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109802"},"PeriodicalIF":3.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810393","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":"BSMPT v3 a tool for phase transitions and primordial gravitational waves in extended Higgs sectors","authors":"Philipp Basler ,&nbsp;Lisa Biermann ,&nbsp;Margarete Mühlleitner ,&nbsp;Jonas Müller ,&nbsp;Rui Santos ,&nbsp;João Viana","doi":"10.1016/j.cpc.2025.109766","DOIUrl":"10.1016/j.cpc.2025.109766","url":null,"abstract":"<div><div>Strong first-order phase transitions (SFOPT) during the evolution of the Higgs potential in the early universe not only allow for the dynamical generation of the observed matter-antimatter asymmetry, they can also source a stochastic gravitational wave (GW) background possibly detectable with future space-based gravitational waves interferometers. As SFOPTs are phenomenologically incompatible with the Standard Model (SM) Higgs sector, the observation of GWs from SFOPTs provides an exciting interplay between cosmology and particle physics in the search for new physics. With the <span>C++</span> code <span>BSMPTv3</span>, we present for the first time a tool that performs the whole chain from the particle physics model to the gravitational wave spectrum. Extending the previous versions <span>BSMPTv1</span> and <span>v2</span>, it traces the phases of beyond-SM (BSM) Higgs potentials and is capable of treating multiple vacuum directions and multi-step phase transitions. During the tracing, it checks for discrete symmetries, flat directions, and electroweak symmetry restoration, and finally reports the transition history. The transition probability from the false to the true vacuum is obtained from the solution of the bounce equation which allows for the calculation of the nucleation, percolation and completion temperatures. The amplitude and characteristic frequencies of the GWs originating from bubble collisions and highly relativistic fluid shells, sound waves and turbulence, are evaluated after the calculation of the thermal parameters at the transition temperature, and finally the signal-to-noise ratio at <span>LISA</span> is provided. The code <span>BSMPTv3</span> is a powerful self-contained tool that comes more than timely and will be of great benefit for investigations of the vacuum structure of the early universe of not only simple but also complicated Higgs potentials involving several vacuum directions, with exciting applications in the search for new physics.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"316 ","pages":"Article 109766"},"PeriodicalIF":3.4,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810395","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}