Computer Physics Communications最新文献

{"title":"A unified stochastic particle method for polyatomic gas mixtures","authors":"Fei Fei ,&nbsp;Donghui Liu ,&nbsp;Lefeng Xie ,&nbsp;Zhiyuan Ren ,&nbsp;Yuan Hu","doi":"10.1016/j.cpc.2026.110029","DOIUrl":"10.1016/j.cpc.2026.110029","url":null,"abstract":"<div><div>Based on the Ellipsoidal–Statistical BGK (ESBGK) model developed by Hild and Pfeiffer [J. Comput. Phys. 514, 113226 (2024)], the unified stochastic particle (USP) method is extended to the simulation of polyatomic gas mixtures. By decomposing the collision term into macroscopic and microscopic components and solving the macroscopic part in conjunction with the particle motion, the USP method achieves the asymptotic-preserving property for the Navier-Stokes equations and second-order accuracy in the fluid limit. The proposed scheme is verified through some 1D and 2D benchmark cases, including Couette flow, Poiseuille flow, Shock wave, and hypersonic flow past a cylinder. The USP method results are in good agreement with the Direct Simulation Monte Carlo (DSMC) data across a wide range of Knudsen numbers. Additionally, the proposed USP method demonstrates superior accuracy and efficiency compared to the traditional stochastic particle (SP) method, making it a more suitable choice for complex multi-scale gas dynamics problems.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110029"},"PeriodicalIF":3.4,"publicationDate":"2026-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974176","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":"M2C: An open-source software for multiphysics simulation of compressible multi-material flows and fluid-structure interactions","authors":"Xuning Zhao ,&nbsp;Wentao Ma ,&nbsp;Shafquat Islam ,&nbsp;Aditya Narkhede ,&nbsp;Kevin Wang","doi":"10.1016/j.cpc.2026.110023","DOIUrl":"10.1016/j.cpc.2026.110023","url":null,"abstract":"&lt;div&gt;&lt;div&gt;M2C (Multiphysics Modeling and Computation) is an open-source software for simulating multi-material fluid flows and fluid-structure interactions under extreme conditions, such as high pressures, high temperatures, shock waves, and large interface deformations. It employs a finite volume method to solve the compressible Navier-Stokes equations and supports a wide range of thermodynamic equations of state. M2C incorporates models of laser radiation and absorption, phase transition, and ionization, coupled with continuum dynamics. Multi-material interfaces are evolved using a level set method, while fluid-structure interfaces are tracked using an embedded boundary method. Advective fluxes across interfaces are computed using FIVER (FInite Volume method based on Exact multi-material Riemann problems). For two-way fluid-structure interaction, M2C is coupled with the open-source structural dynamics solver Aero-S using a partitioned procedure. The M2C code is written in C++ and parallelized with MPI for high-performance computing. The source package includes a set of example problems for demonstration and user training. Accuracy is verified through benchmark cases such as Riemann problems, interface evolution, single-bubble dynamics, and ionization response. Several multiphysics applications are also presented, including laser-induced thermal cavitation, explosion and blast mitigation, and hypervelocity impact.&lt;/div&gt;&lt;div&gt;&lt;strong&gt;PROGRAM SUMMARY&lt;/strong&gt; &lt;em&gt;Program Title:&lt;/em&gt; M2C (Multiphysics Modeling and Computation)&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/gdjrrjwgf4.1&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/kevinwgy/m2c&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; C++&lt;/div&gt;&lt;div&gt;&lt;em&gt;Supplementary material:&lt;/em&gt; The M2C package includes a suite of test cases that illustrate the software’s capabilities. These examples can also serve as templates for setting up new simulations.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem:&lt;/em&gt; This work addresses the analysis of multi-material fluid flow and fluid-structure interaction problems under conditions involving high pressure, high velocity, high temperature, or a combination of them. In such problems, material compressibility and thermodynamics play a significant role, and the system may exhibit shock waves, large structural deformations, and large deformation of fluid material subdomains. Unlike conventional fluid dynamics problems, the boundaries of the fluid domain and material subdomains are time-dependent, unknown in advance, and must be determined as part of the analysis. Across material interfaces, some state variables (e.g., density) may exhibit jumps of several orders of magnitude, while others (e.g., normal velocity) remain continuous. Some problems may also involve strong ","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110023"},"PeriodicalIF":3.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145973838","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":"Full EPIC-GOD: An energy-conserving full particle-in-cell code for GPU acceleration using OpenACC","authors":"Sunjung Kim ,&nbsp;G.S. Choe ,&nbsp;Dongsu Ryu ,&nbsp;Sibaek Yi","doi":"10.1016/j.cpc.2026.110021","DOIUrl":"10.1016/j.cpc.2026.110021","url":null,"abstract":"<div><div>We present Full EPIC-GOD, a fully implicit, energy- and charge-conserving electromagnetic particle-in-cell (PIC) code. Unlike conventional full-PIC approaches that often suffer from numerical violations of conservation laws, Full EPIC-GOD tightly couples particle dynamics with Maxwell’s equations via an iterative predictor-corrector scheme. Charge conservation is rigorously enforced through Esirkepov’s method, while total energy conservation is achieved using consistent field interpolation and residual-based iterative convergence.</div><div>The algorithm employs second-order accurate discretization in both space and time and supports adaptive time stepping to enhance numerical stability and efficiency. The code is parallelized with OpenACC and demonstrates strong scaling on multi-GPU systems, enabling large-scale kinetic simulations.</div><div>We validate the code using standard benchmarks involving kinetic waves, electromagnetic instabilities, and magnetic reconnection. The results show excellent agreement with theory and prior simulations, confirming the method’s accuracy, stability, and conservation properties. Designed for fully kinetic plasma simulations, Full EPIC-GOD enables high-fidelity modeling of collisionless plasma dynamics across microscopic to relativistic regimes in space and astrophysical environments.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110021"},"PeriodicalIF":3.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034993","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":"DynHeMat: A program for zero-point averaged dynamics of pure and doped helium nanodroplets","authors":"David A. Bonhommeau","doi":"10.1016/j.cpc.2025.110014","DOIUrl":"10.1016/j.cpc.2025.110014","url":null,"abstract":"&lt;div&gt;&lt;div&gt;DynHeMat is a parallel program aimed at modeling the dynamics of pure and doped helium nanodroplets (HNDs) by means of zero-point averaged dynamics (ZPAD), a method where the quantum nature of helium atoms is taken into account through the use of a He-He pseudopotential which includes zero-point effects of helium clusters on an average manner. Three He-He pseudopotentials, defined for applications in different contexts, are implemented. Large HNDs can be formed by successive coalescences of smaller HNDs keeping in mind that, depending on the HND size and He-He pseudopotential in use, the liquid character of the HND is more or less pronounced. Files containing the positions and velocities of HNDs formed with the three aforementioned He-He pseudopotentials are collected in a local databank, called ZPAD_DB. ZPAD simulations can be carried out at constant energy or temperature, then enabling the user to investigate collision, coagulation or submersion processes in pure or doped HNDs. Impurities can be rare-gas atoms (Ne, Ar, Kr, Xe and Rn), alkali atoms (Li, Na, K, Rb, Cs), or homogeneous clusters composed of such atoms. The program provides information on trajectories, namely positions, velocities, energies, radial distribution functions, and the initial distribution of HND surface atoms. Extension to other impurities or He-He pseudopotentials is made possible by the current structure of the program and keyword system.&lt;/div&gt;&lt;div&gt;PROGRAM SUMMARY&lt;/div&gt;&lt;div&gt;&lt;em&gt;Program title&lt;/em&gt;: DynHeMat&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/10.17632/3hrfykstvr.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 (GPL)&lt;/div&gt;&lt;div&gt;&lt;em&gt;Programming language&lt;/em&gt;: Fortran 90&lt;/div&gt;&lt;div&gt;&lt;em&gt;Nature of problem&lt;/em&gt;:&lt;/div&gt;&lt;div&gt;Helium nanodroplets (HNDs) are large quantum systems containing from a few thousands to billion atoms. The more quantum approaches, like quantum Monte Carlo, time-dependent density functional theory and path integral molecular dynamics, are often limited to the treatment of a few hundreds or thousands atoms or to small statistics in terms of projectile velocities or impact parameters, for instance. On the contrary, a classical approach would enable simulations on larger systems provided that the quantum nature of helium atoms is included on an average manner in the calculation in order to ensure that the expected heliophilic or heliophobic nature of impurities can be maintained.&lt;/div&gt;&lt;div&gt;&lt;em&gt;Solution method&lt;/em&gt;:&lt;/div&gt;&lt;div&gt;The zero-point averaged dynamics (ZPAD) includes zero-point effects on an average manner in classical simulations through the use of an effective He-He potential, and possibly effective He-impurity potentials, which makes the HND liquid and drastically improves the agreement with quantum calculations compared to standard classical simulations. Initially used to tackle the fragmentation of rare-gas clusters embedded in HNDs io","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110014"},"PeriodicalIF":3.4,"publicationDate":"2026-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034923","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 projection method for particle resampling","authors":"Mark F. Adams ,&nbsp;Daniel S. Finn ,&nbsp;Matthew G. Knepley ,&nbsp;Joseph V. Pusztay","doi":"10.1016/j.cpc.2026.110024","DOIUrl":"10.1016/j.cpc.2026.110024","url":null,"abstract":"<div><div>Particle discretizations of partial differential equations are advantageous for high-dimensional kinetic models in phase-space due to their better scalability than continuum approaches with respect to dimension. Complex processes collectively referred to as <em>particle noise</em> hamper long time simulations with particle methods. One approach to address this problem is particle mesh adaptivity, or remapping, known as <em>particle resampling</em> and <em>remeshing</em>. This work introduces a resampling method that projects particles to and from a (finite element) function space. The method is simple, using standard sparse linear algebra and finite element techniques, and it preserves all moments up to the order of a polynomial represented exactly by the continuum function space. It is distinguished from most other mesh-based methods in that new particle positions and number are decoupled from the mesh, allowing particle and continuum meshes to be adapted relatively independently. While this work is developed with structured particle and continuum phase-space grids on 1<em>X</em> + 1<em>V</em> Vlasov-Poisson models of Landau damping and two-stream instability, the method is well-suited to unstructured grids. Stable long time dynamics are demonstrated up to time <span><math><mrow><mi>T</mi><mo>=</mo><mn>500</mn></mrow></math></span>. Reproducibility artifacts and data are publicly available.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110024"},"PeriodicalIF":3.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146034992","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":"Object-oriented programming as a tool for constructing high-order quantum-kinetic BBGKY equations","authors":"Ekaterina A. Tarasevich ,&nbsp;Maxim G. Gladush","doi":"10.1016/j.cpc.2026.110020","DOIUrl":"10.1016/j.cpc.2026.110020","url":null,"abstract":"<div><div>Theoretical methods based on the density matrix provide powerful tools for describing open quantum systems. However, such methods are complicated and intricate to be used analytically. Here we present an object-oriented framework for constructing the equation of motion of the correlation matrix at a given order within the quantum BBGKY hierarchy, which is widely used to describe the interaction of many-particle systems. The algorithm of machine derivation of equations includes the implementation of the principles of quantum mechanics and operator algebra. It is based on the description and use of classes in the Python programming environment. Class objects correspond to the elements of the equations that are derived: density matrix, correlation matrix, energy operators, commutator and several operators indexing systems. The program contains a special class that allows one to define a statistical ensemble with an infinite number of subsystems. For all classes, methods implementing the actions of the operator algebra are specified. The number of subsystems of the statistical ensemble for the physical problem and the types of subsystems between which pairwise interactions are possible are specified as an input parameters. It is shown that this framework allows one to derive the equations of motion of the fourth-order correlation matrix in less than one minute.</div><div><strong>Program summary</strong></div><div><em>Program title</em>: Program for symbolic generation of kinetic equations in quantum Bogolyubov hierarchies (BBGKY).</div><div><em>CPC Library link to program files</em>: <span><span>https://doi.org/10.17632/f97bwbypfd.1</span><svg><path></path></svg></span></div><div><em>Licensing provisions</em>: GNU General Public License 3</div><div><em>Programming language</em>: Python 3.10</div><div><em>Nature of problem</em>: Construction of Bogolyubov hierarchies for reduced many-particle density matrices and correlation matrices is a powerful tool for solving problems in Physics. However, the analytical derivation of equations requires considerable time and effort to avoid multiple errors. Bogolyubov hierarchies for problems in quantum optics is a novel approach and requires special attention.</div><div><em>Solution method</em>: In order to solve this problem, we used object-oriented programming. Each element of quantum-mechanical object (operator, density matrix, correlation matrix, etc.) is assigned to a class with specified attributes and methods. The attributes and methods of each class represent operations of quantum-mechanical algebra. This allows one to perform all the necessary operations on a computer and significantly reduce the time to obtain error-free output.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110020"},"PeriodicalIF":3.4,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146073663","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":"Accurate calculation of the gradients of the equilibrium poloidal flux in tokamaks","authors":"M. Woo,&nbsp;G. Jo,&nbsp;B.H. Park,&nbsp;A.Y. Aydemir,&nbsp;J.-H Kim","doi":"10.1016/j.cpc.2026.110022","DOIUrl":"10.1016/j.cpc.2026.110022","url":null,"abstract":"<div><div>This paper presents a novel method for calculating the first, second, and third derivatives of the equilibrium poloidal flux in different directions in tokamaks. The method is implemented in a new code called Equilibrium Derivative in Arbitrary Mesh (EDAM) which is designed for practical fusion applications. The spectral method is adopted along the boundary with evenly spaced angles, while unstructured triangular meshes are used inside the computational domain. A new boundary integral equation (BIE) is derived and solved numerically to obtain the first and higher-order derivatives at the boundary. Using GS equation, linear partial differential equations for the first and higher-order flux derivatives are then constructed and solved. Validation is performed using an analytical equilibrium constructed by Cicogna, which describes D-shaped plasmas with steep profiles near the boundary. The code demonstrates similar convergence rates for the first and higher-order derivatives, achieving second order accuracy. This new method has significant potential for practical fusion simulations, providing derivatives up to the third order with the required accuracy and precisely given values at any nodal points of the unstructured mesh.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110022"},"PeriodicalIF":3.4,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974174","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":"Exploring ISR phase space in proton-proton collision with adaptive grid and veto algorithms","authors":"M.H. Heraiz ,&nbsp;E. Redouane-Salah","doi":"10.1016/j.cpc.2025.110017","DOIUrl":"10.1016/j.cpc.2025.110017","url":null,"abstract":"<div><div>In this work, we developed an adaptive grid algorithm to integrate the splitting probability distribution in initial state radiation (ISR) for Large Hadrons Collider (LHC) collisions. By employing a dynamically refined grid, the method concentrates computational resources on regions of physical significance, while avoiding divergence-prone areas associated with collinear and soft emissions. A veto algorithm manages these regions effectively. This approach enhances the accuracy of Monte Carlo simulations, enabling robust phase space generation while minimizing computational cost.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110017"},"PeriodicalIF":3.4,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974297","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":"DFODE-Kit: Deep learning package for solving flame chemical kinetics with high-dimensional stiff ordinary differential equations","authors":"Han Li ,&nbsp;Ke Xiao ,&nbsp;Yangchen Xu ,&nbsp;Haoze Zhang ,&nbsp;Zhenyi Chen ,&nbsp;Runze Mao ,&nbsp;Zhi X. Chen","doi":"10.1016/j.cpc.2025.110013","DOIUrl":"10.1016/j.cpc.2025.110013","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Recent advances in deep learning for solving flame chemical kinetics offer promising solutions to the long-standing trade-off between accuracy and computational efficiency in combustion simulations. This work introduces DFODE-kit, an open-source Python package designed to replace the conventional, computationally intensive integration of chemical source terms governed by high-dimensional, stiff ordinary differential equations (ODEs), thereby substantially accelerating chemistry evaluation in combustion simulations. The package provides: i) an efficient sampling module that extracts high-quality thermochemical states from low-dimensional manifolds in canonical flames; ii) an effective data augmentation module that enriches the dataset to approximate the high-dimensional composition space encountered in turbulent flames; and (iii) an optimized neural network training module with multiscale preprocessing and physics-informed constraints to enhance model fidelity and stability. The trained models are seamlessly integrated into our previously released CFD solver DeepFlame&lt;span&gt;&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt;&lt;/span&gt;, and can also be adapted for use with other widely used platforms such as OpenFOAM via custom interface modifications. Illustrative examples for &lt;em&gt;a posteriori&lt;/em&gt; validations demonstrate that DFODE-kit models achieve excellent predictive accuracy. Furthermore, in isolated chemistry evaluations, the DNN models attain up to &lt;em&gt;O&lt;/em&gt;(10&lt;sup&gt;2&lt;/sup&gt;) acceleration compared with CVODE, while end-to-end CFD runs typically see multi-fold speed-ups. The package, dataset, and example scripts are released to support reproducible benchmarking and community adoption. &lt;strong&gt;PROGRAM SUMMARY&lt;/strong&gt;&lt;em&gt;Program Title:&lt;/em&gt; DFODE-kit &lt;em&gt;CPC Library link to program files:&lt;/em&gt; (to be added by Technical Editor) &lt;em&gt;Developer’s repository link:&lt;/em&gt; &lt;span&gt;&lt;span&gt;https://github.com/deepflame-ai/DFODE-kit&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt; &lt;em&gt;Licensing provisions:&lt;/em&gt; GPLv3 &lt;em&gt;Programming language:&lt;/em&gt; Python &lt;em&gt;Nature of problem:&lt;/em&gt;In combustion systems, chemical source terms are governed by stiff ODEs, where stiffness arises from the inherent multiscale nature of chemical kinetics. Specifically, the vastly disparate timescales between fast and slow reactions, combined with strong nonlinear coupling among species, give rise to numerically stiff systems that require extremely small time steps for stable and accurate integration. As a result, ODE integration often dominates the computational cost of high-fidelity reacting flow simulations, limiting their scalability and physical resolution. &lt;em&gt;Solution method:&lt;/em&gt; To address the computational challenges posed by stiff chemical ODE integration, deep learning provides a promising alternative, owing to its powerful nonlinear regression capabilities. When trained on high-fidelity thermochemical datasets, deep learning models can accurately approximate the complex relationships between thermoch","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110013"},"PeriodicalIF":3.4,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145923460","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 new database website for nuclear level densities","authors":"Chirag Rathi ,&nbsp;Alexander Voinov ,&nbsp;Kyle Godbey ,&nbsp;Zach Meisel ,&nbsp;Kristen Leibensperger","doi":"10.1016/j.cpc.2025.110018","DOIUrl":"10.1016/j.cpc.2025.110018","url":null,"abstract":"<div><div>We introduce a new open-access, web-based database (<span><span>http://nld.ascsn.net</span><svg><path></path></svg></span>), Current Archive of Nuclear Density of Levels (CANDL), that hosts experimental nuclear level density (NLD) datasets from a variety of techniques and energy ranges. Built using the Dash framework in Python, the database is designed to be interactive and user-friendly, allowing researchers to search, visualize, fit, and export NLD data with minimal effort. This resource includes data extracted from evaporation spectra, Oslo method variants, and other experimental techniques that cover excitation energies beyond the neutron resonance region. The database supports on-the-fly fitting with two widely-used phenomenological models—the Constant Temperature (CT) model and the Back-Shifted Fermi Gas (BSFG) model—selected for their simplicity and computational efficiency. Future versions aim to include additional datasets and model types, as well as easy-to-use interfaces to data science techniques. This platform offers a vital tool for the nuclear physics, astrophysics, medicine, and reactor design communities.</div></div>","PeriodicalId":285,"journal":{"name":"Computer Physics Communications","volume":"321 ","pages":"Article 110018"},"PeriodicalIF":3.4,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145883411","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}