Computer Physics Communications

The suggested package FUMILIM, based on famous FUMILI minimization package, has the following advantages. Unlimited number of parameters (multi-set tasks). Ability to work with multidimensional experimental points, described by a vector function. The preliminary scan is envisaged for complicated tasks. For heavy user's functions the parallel fit is envisaged with the help of OpenMP service. Multi-optional user interface, including option to ignore wrong experimental points. The package contains popular intrinsic user's functions. All of them can be used without the definition of the parameter initial values. There is a package of fast track reconstruction programs for working with detectors, including drift chambers. The capacity of these programs is about of tracks per second (at 2.8\thinspace GHz). In the corrected version the - version of the scan procedure has been replaced by a stable new one. A number of other improvements have also been made. All programmes are written in FORTRAN-90. The investigation has been performed at the Veksler and Baldin Laboratory of High Energy Physics, JINR.

The four-state method [{ Phys. Rev. B , 224,429 (2011)}] is a reliable approach for calculating parameters of spin Hamiltonian in magnetic materials. However, the conventional self-consistent implementations often suffer from convergence difficulty and high computational costs. Here, we propose a method to generate appropriate initial spin densities that significantly reduces the number of the required self-consistent iterations. Remarkably, we find that this initial distribution even enables the non-self-consistent calculations to yield reasonable results. To facilitate the application of this method, we have developed Spinss, an open-source code that creates the initial spin densities and other necessary files for both self-consistent and non-self-consistent density functional theory calculations. We provide a detailed description of the algorithm, input and output files, and application examples which demonstrate the effectiveness of Spinss for both bulk and low-dimensional systems.

Nexus-CAT (Cluster Analysis Toolkit) is an open-source Python package for cluster detection and percolation analysis of atomistic simulation trajectories. Standard structural tools, such as the pair distribution function or structure factor, fail to capture the long-range connectivity changes underlying amorphous-amorphous transitions in glassy materials. Nexus-CAT addresses this gap by reading extended XYZ trajectory files and identifying clusters via a Union-Find algorithm with path-compression. Four clustering strategies, i.e., distance-based, bonding, coordination-filtered, and shared-neighbor, are implemented through a Strategy Factory design pattern, enabling the treatment of diverse network topologies. The program computes key percolation properties with percolation detection based on a rigorous period vector algorithm. The package is validated against theoretical predictions and applied to glasses with different bonding environments, namely vitreous silica, vitreous ice, and amorphous silicon. One original result is the observation of a percolation transition prior to crystallization in the latter, indicating that pressure-induced crystallization is initially driven by an amorphous transformation with similar coordination number. The code is also designed to be readily extended to gels, cements, and other disordered materials. Nexus-CAT is fully available on GitHub and PyPI.

We present BO-PBK (BO-Product-Bi-Kappa), a new solver for kinetic dispersion relations of obliquely propagating waves in magnetized plasmas with complex velocity distributions. It reformulates the linearized Vlasov-Maxwell system into a compact eigenvalue problem, enabling direct computation of multiple wave branches and unstable modes without iterative initial-value searches. Key innovations include a unified framework supporting product-bi-kappa, kappa-Maxwellian, Maxwellian-kappa, bi-Maxwellian, and hybrid distributions with multi-component and loss-cone features; a concise rational-form eigenvalue formulation; and a 2-3 times reduction in matrix dimensions compared to the BO-KM solver, with improved efficiency at larger kappa indices. Benchmark tests confirm accurate reproduction of standard kinetic results and efficient resolution of waves and instabilities. BO-PBK thus provides a computationally efficient tool for wave and stability analysis in space and laboratory plasmas.

This paper presents the Parallel Coupler for Multimodel Simulations (PCMS), a new GPU accelerated generalized framework for coupling simulation codes on leadership class supercomputers. PCMS includes distributed control and field mapping methods for up to five dimensions. For field mapping, PCMS can utilize discretization and field information to accommodate physics constraints. PCMS is demonstrated with a coupling of the gyrokinetic microturbulence code XGC with a Monte Carlo neutral transport code DEGAS2 and with a 5D distribution function coupling of an energetic particle transport code (GNET) to a gyrokinetic microturbulence code (GTC). A scaling study is also presented to stress the PCMS APIs and underlying rendezvous-based communication protocol implemented with ADIOS2. It demonstrates the high performance of the ADIOS2 SST engine using remote direct memory access versus the filesystem-based BP4 engine on up to 260 nodes of Frontier; 16 to 2048 processes for the application being scaled and a fixed 16 processes each for the coupler and the second application.

This paper introduces an efficient tensor-vector product technique for the fast and accurate approximation of integral operators within physics-informed deep learning frameworks. Our approach leverages Kolmogorov-Arnold networks to evaluate problem dynamics at specific points, while employing Gaussian quadrature formulas to approximate the integral components, even in the presence of semi-infinite domains or singularities. We demonstrate the applicability of the proposed method to both Fredholm and Volterra integral operators, as well as to optimal control problems involving continuous time. Additionally, we outline how this approach can be extended to approximate fractional derivatives and integrals and propose a fast matrix-vector product algorithm for efficiently computing the fractional Caputo derivative. In the numerical section, we conduct comprehensive experiments on forward and inverse problems. For forward problems, we evaluate the performance of our method on over 50 diverse mathematical problems, including multi-dimensional integral equations, systems of integral equations, partial and fractional integro-differential equations, and various optimal control problems in delay, fractional, multi-dimensional, and nonlinear configurations. For inverse problems, we test our approach on several integral equations and fractional integro-differential problems. Finally, we introduce the pinnies Python package to facilitate the implementation and usability of the proposed method.

A Fortran program that can be applied to simulate the four-dimensional dynamical structure factors (Dysurf) for inelastic neutron and inelastic X-ray scattering experiments is presented. With the underlying theoretical formalism, the detailed implementation of the program is described. Based on the second-order force constants from the first-principles method, the Dysurf code can well reproduce the measured spectroscopies of those scattering experiments. Four main applications of this code with the corresponding examples are introduced here, including the multi-dimensional dynamical structure factors, thermal diffuse scattering, line cut at specific points in the Brillouin zone and sample design. This program will be helpful in terms of designing and explaining related inelastic scattering experiments.

Accurate modeling of thermal and non-thermal plasma systems requires reliable thermodynamic and transport properties, which are often difficult to measure or unavailable for many plasma mixtures and operating conditions. These quantities have a strong influence on the predictive quality of simulations. Current methods to compute these quantities for modeling may often lack the physical consistency required for high-fidelity applications, and computing them from very first principles can be cumbersome as they are the result of a complex problem that has to be systematically addressed. To overcome current challenges in computing plasma properties, we present PPFM (Plasma Properties For Many), an open-source C++ library for the computation of plasma thermodynamic and transport properties in both local and non-local thermodynamic equilibrium, LTE and NLTE, respectively. PPFM combines well-established theoretical models with a modular and advanced object-oriented architecture designed for flexibility, extensibility and scalability while ensuring minimal and intuitive user inputs. PPFM offers a promising reference platform to address thermodynamic and transport properties determinations, starting from very basic physical principles and microscopic properties, to compute macroscopic quantities.

Algorithmic formulations of GPU programs provide a high-level alternative to device-specific code by expressing computations as compositions of well-defined parallel primitives (e.g., map, sort, reduce), rather than through handcrafted GPU kernels. In this work, we demonstrate that this paradigm can be extended to complex and challenging problems in computational physics: the simulation of granular flows and fluid-particle interactions. LEDDS, our open-source framework, performs fully coupled Lattice Boltzmann – Discrete Element Method (LBM-DEM) simulations using only algorithmic primitives, and runs efficiently on single-GPU platforms. The entire workflow, including neighbor search, collision detection, and fluid-particle coupling, is expressed as a sequence of portable primitives. Performance results are primarily reported for an NVIDIA A100 GPU, while portability to AMD GPUs and CPUs is also demonstrated. The code relies on an abstraction layer that dispatches generic algorithms to platform-specific function calls. Most operations are handled by the C++ parallel algorithms layer, which provides a sufficient abstraction by itself, while in selected cases a backend-specific variant is chosen for performance reasons, using either the Thrust library or AMD's rocThrust layer. LEDDS is validated through benchmarks spanning both DEM and LBM-DEM configurations, including sphere and ellipsoid collisions, wall friction tests, single-particle settling, Jeffery's orbits, and particle-laden shear flows. Despite its high level of abstraction, LEDDS achieves performance comparable to those of hand-tuned CUDA solvers, while maintaining portability and code clarity. These results show that high-performance LBM-DEM coupling can be achieved without sacrificing generality or readability, establishing LEDDS as a blueprint for portable multiphysics frameworks based on algorithmic primitives.

We introduce kalypsso (a Kokkos Applicative LaYer for Parallel and Scalable Solvers on Octrees): a new octree-based block-structured adaptive mesh refinement (AMR) framework using the C++ kokkos library for designing performance portable applications in computational fluid dynamics (CFD). Mesh management in distributed memory is implemented with the help of the p4est library, which provides a MPI parallel CPU implementation of the forest of octrees AMR algorithms. All heavyweight application data are allocated on a computing device, either a CPU or a GPU, and managed directly by kalypsso. One of the key design choice of kalypsso architecture is to use a lightweight hash-table-based (or dictionary) data structure for exchanging mesh geometry information between p4est, running on the host CPU, and the computational kernels executed on the accelerated device. Several finite volume methods for compressible monofluid and bifluid hydrodynamics, as well as magnetohydrodynamics are implemented using the kokkos programing model for exploiting shared memory parallelism on most existing CPU and GPU-based architecture. Node-level performance metrics for a second-order MUSCL-Hancock finite volume solver are measured to evaluate the impact of the size of the grid of cells attached onto octree leaves. A single Nvidia GH200 GPU can perform about 1.4 billions cell-updates per second. The performance portability on a cluster of CPU and GPU is demonstrated; a node-to-node weak scaling efficiency of  ∼ 80% is obtained on a cluster of 512 Nvidia GH200 GPUs. Using comparable hardware resources and considering the Euler equation solver in kalypsso with AMR activated, a consistent  × 5 CPU to GPU time to solution speed-up is obtained.