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Computer Physics Communications

ISSN: 0010-4655

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Datasets associated with articles published in Computer Physics Communications

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1970
2025
1970 2025
6044 results
  • ERMES 20.0: Open-source finite element tool for computational electromagnetics in the frequency domain
    ERMES 20.0 is an open-source software which solves the Maxwell's equations in frequency domain with the Finite Element Method (FEM). The new ERMES 20.0 is a significant upgrade from the previous ERMES 7.0 [1]. It introduces new features, modules, and FEM formulations to address the challenging problems commonly encountered in the design and analysis of nuclear fusion reactors [2]. Key additions are the electrostatic and cold plasma module, along with new FEM formulations as the stabilized double-curl edge element formulation [3] and the local L^2 projection method with nodal and bubble elements [4,5]. Furthermore, all the formulations now include an A-V potentials version. The ample set of methods available in the new ERMES 20.0 allows the user to select the most suitable FEM formulation to generate the best possible conditioned matrix for each specific problem. ERMES 20.0 operates in the static, quasi-static and the high-frequency regimens, making it a versatile tool which can be used in a wide variety of situations. For instance, it had been applied to microwave engineering, bioelectromagnetics, and electromagnetic compatibility. Now, thanks to the new electrostatic and cold plasma modules, the range of applications has been extended to relevant nuclear fusion engineering problems as: the computation of induced forces, plasma control, probability estimation of electric arc initiation, current distribution in arbitrary geometries, and the study of electromagnetic wave-plasma-wall interactions inside a fusion reactor. ERMES 20.0 is available for Windows and Linux systems and it has improved its capabilities to solve large problems on High Performance Computing (HPC) infrastructures thanks to its new interface with the solver libraries PETSc [6] and Python NumPy [7]. As in previous versions, ERMES 20.0 features a graphical user-friendly interface integrated into the pre- and post-processor GiD [8]. GiD handles geometrical modeling, data input, meshing, and result visualization. ERMES 20.0 is licensed under the open-source software 2-clause BSD license. This document is accompanied by a comprehensive manual that provides a step-by-step installation guide, a detailed description of all the new features and formulations, as well as the executables, user interface, examples, and source code of ERMES 20.0.
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  • KinetiX: A performance portable code generator for chemical kinetics and transport properties
    We present KinetiX, a software toolkit to generate computationally efficient fuel-specific routines for the chemical source term, thermodynamic and mixture-averaged transport properties for use in combustion simulation codes. The C++ routines are designed for high-performance execution on both CPU and GPU architectures. On CPUs, chemical kinetics computations are optimized by eliminating redundant operations and using data alignment and loops with trivial access patterns that enable auto-vectorization, reducing the latency of complex mathematical operations. On GPUs, performance is improved by loop unrolling, reducing the number of costly exponential evaluations and limiting the number of live variables for better register usage. The accuracy of the generated routines is checked against reference values computed using Cantera and the maximum relative errors are below 10^-7. We evaluate the performance of the kernels on some of the latest CPU and GPU architectures from AMD and NVIDIA, i.e., AMD EPYC 9653, AMD MI250X, and NVIDIA H100. The routines generated by KinetiX outperform the general-purpose Cantera library, achieving speedups of up to 2.4x for species production rates and 3.2x for mixture-averaged transport properties on CPUs. Compared to the routines generated by PelePhysics (CEPTR), KinetiX achieves speedups of up to 2.6x on CPUs and 1.7x on GPUs for the species production rates kernel on a single-threaded basis.
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  • chemtrain: Learning deep potential models via automatic differentiation and statistical physics
    Neural Networks (NNs) are effective models for refining the accuracy of molecular dynamics, opening up new fields of application. Typically trained bottom-up, atomistic NN potential models can reach first-principle accuracy, while coarse-grained implicit solvent NN potentials surpass classical continuum solvent models. However, overcoming the limitations of costly generation of accurate reference data and data inefficiency of common bottom-up training demands efficient incorporation of data from many sources. This paper introduces the framework chemtrain to learn sophisticated NN potential models through customizable training routines and advanced training algorithms. These routines can combine multiple top-down and bottom-up algorithms, e.g., to incorporate both experimental and simulation data or pre-train potentials with less costly algorithms. chemtrain provides an object-oriented high-level interface to simplify the creation of custom routines. On the lower level, chemtrain relies on JAX to compute gradients and scale the computations to use available resources. We demonstrate the simplicity and importance of combining multiple algorithms in the examples of parametrizing an all-atomistic model of titanium and a coarse-grained implicit solvent model of alanine dipeptide.
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  • DWR-drag: A new generation software for the double wall-ring interfacial shear rheometer's data analysis
    The double wall-ring (DWR) rotational configuration is nowadays the instrument of choice regarding interfacial shear rheometers (ISR) in rotational configurations. Complex numerical schemes must be used in the analysis of the output data in order to appropriately deal with the coupling between interfacial and bulk fluid flows, and to separate viscous and elastic contribution or the interfacial response. We present a second generation code for analyzing the interfacial shear rheology experimental results of small amplitude oscillatory measurements made with a DWR rotational rheometer. The package presented here improves significantly the accuracy and applicability range of the previous available software packages by implementing: i) a physically motivated iterative scheme based on the probe's equation of motion, ii) an increased user selectable spatial resolution, and iii) a second order approximation for the velocity gradients at the ring surfaces. Moreover, the optimization of the computational effort allows, in many cases, for on-the-fly execution during data acquisition in real experiments.
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  • A brief introduction to PACIAE 4.0
    Parton And-hadron China Institute of Atomic Energy (PACIAE) is a multipurpose Monte Carlo event generator developed to describe a wide range of high-energy collisions, including lepton-lepton, lepton-hadron, lepton-nucleus, hadron-hadron, hadron-nucleus, and nucleus-nucleus collisions. It is built based on the PYTHIA program, and incorporates parton and hadron cascades to address the nuclear medium effects. PACIAE 4.0 is the new generation of PACIAE model surpassing the version 3.0. In PACIAE 4.0, the old fixed-format FORTRAN 77 code has been refactored and rewritten by the free-format modern Fortran and C++ languages. The C++-based PYTHIA 8.3 is interfaced in, while previous versions connected to the Fortran-based PYTHIA 6.4 only. Several improvements are also introduced, which enable PACIAE 4.0 to contain more physics and features to model the high-energy collisions. This is the first attempt to transition PACIAE from Fortran to C++.
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  • TorchQC - A framework for efficiently integrating machine and deep learning methods in quantum dynamics and control
    Machine learning has been revolutionizing our world over the last few years and is also increasingly exploited in several areas of physics, including quantum dynamics and control. The need for a framework that brings together machine learning models and quantum simulation methods has been quite high within the quantum control field, with the ultimate goal of exploiting these powerful computational methods for the efficient implementation of modern quantum technologies. The existing frameworks for quantum system simulations, such as QuTip and QuantumOptics.jl, even though they are very successful in simulating quantum dynamics, cannot be easily incorporated into the platforms used for the development of machine learning models, like for example PyTorch. The TorchQC framework introduced in the present work comes exactly to fill this gap. It is a new library written entirely in Python and based on the PyTorch deep learning library. PyTorch and other deep learning frameworks are based on tensors, a structure that is also used in quantum mechanics. This is the common ground that TorchQC utilizes to combine quantum physics simulations and deep learning models. TorchQC exploits PyTorch and its tensor mechanism to represent quantum states and operators as tensors, while it also incorporates all the tools needed to simulate quantum system dynamics. All necessary operations are internal in the PyTorch library, thus TorchQC programs can be executed in GPUs, substantially reducing the simulation time. We believe that the proposed TorchQC library has the potential to accelerate the development of deep learning models directly incorporating quantum simulations, enabling the easier integration of these powerful techniques in modern quantum technologies.
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  • A universal implementation of radiative effects in neutrino event generators
    Due to the similarities between electron-nucleus (eA) and neutrino-nucleus scattering (νA), eA data can contribute key information to improve cross-section modeling in eA and hence in νA event generators. However, to compare data and generated events, either the data must be radiatively corrected or radiative effects need to be included in the event generators. We implemented a universal radiative corrections program that can be used with all reaction mechanisms and any eA event generator. Our program includes real photon radiation by the incident and scattered electrons, and virtual photon exchange and photon vacuum polarization diagrams. It uses the “extended peaking” approximation for electron radiation and neglects charged hadron radiation. This method, validated with GENIE, can also be extended to simulate νA radiative effects. This work facilitates data-event-generator comparisons used to improve νA event generators for the next-generation of neutrino experiments.
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  • SMIwiz-2.0: Extended functionalities for wavefield decomposition, linearized and nonlinear inversion
    We extend the functionalities of SMIwiz open source software to include up-down wavefield separation, reflection waveform inversion, as well as linearized waveform inversion in data and image domain. The fundamental functionalities for 2D/3D wave modelling and imaging (reverse time migration and nonlinear full waveform inversion) are backward compatible with improvements in seismic imaging processing. Reproducible examples are supplied to verify these developments.
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  • CUBENS: A GPU-accelerated high-order solver for wall-bounded flows with non-ideal fluids
    We present a massively parallel GPU-accelerated solver for direct numerical simulations of transitional and turbulent flat-plate boundary layers and channel flows involving fluids in non-ideal thermodynamic states. While several high-fidelity solvers are currently available as open source, all of them are restricted to the ideal-gas region. In contrast, the CUBic Equation of state Navier-Stokes solver (CUBENS) can accurately model and simulate the non-ideal thermodynamics of single-phase compressible fluids in the vicinity of the vapor-liquid saturation line or the thermodynamic critical point. By employing high-order finite-difference schemes and convective terms in split, kinetic-energy-, and entropy-preserving form, the solver is numerically stable, and robust with minimal numerical dissipation, enabling it to capture the steep variations of non-ideal thermodynamic properties. For cost-effective high-fidelity simulations, in addition to MPI parallelization, CUBENS is GPU-accelerated using OpenACC directives for computation offloading, and asynchronous GPU-aware MPI for efficient GPU-GPU communication. Moreover, CUBENS is compatible with both NVIDIA and AMD GPU architectures, achieving significant performance results while ensuring energy-efficient simulations. For instance, using 64 NVIDIA A100 GPUs compared to 8192 CPUs at the same computational cost results in a speedup of approximately 130×. In multi-node and multi-GPU configurations ranging from 2 to 128 compute nodes (8 to 512 GPUs), a strong scaling efficiency of around 52% and a weak scaling efficiency of 0.88 with 10243 points per GPU, corresponding to approximately 5 billion degrees of freedom, are achieved. The CUBENS solver is validated against selected cases from the literature, covering transitional to turbulent ideal and non-ideal flows up to the transonic regime. In particular, we demonstrate the solver's suitability and applicability for direct numerical simulations of transitional boundary layers with fluids at supercritical pressure and with buoyancy effects. The development of this high-fidelity solver offers the potential for future fundamental research in non-ideal compressible fluid dynamics.
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  • LibRPA: A software package for low-scaling first-principles calculations of random phase approximation electron correlation energy based on numerical atomic orbitals
    LibRPA is a software package designed for efficient calculations of random phase approximation (RPA) electron correlation energies from first principles using numerical atomic orbital (NAOs). Leveraging a localized resolution of identity (LRI) technique, LibRPA achieves O(N^2) or better scaling behavior, making it suitable for large-scale calculation of periodic systems. Implemented in C++ and Python with MPI/OpenMP parallelism, LibRPA integrates seamlessly with NAO-based density functional theory (DFT) packages through flexible file-based and API-based interfaces. In this work, we present the theoretical framework, algorithm, software architecture, and installation and usage guide of LibRPA. Performance benchmarks, including the parallel efficiency with respect to the computational resources and the adsorption energy calculations for H20 molecules on graphene, demonstrate its nearly ideal scalability and numerical reliability. LibRPA offers a useful tool for RPA-based calculations for large-scale extended systems.
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