Computer Physics Communications

This paper introduces an improved version of our previous CUDA programs [1] for solving the dipolar Gross-Pitaevskii equation in three spatial dimensions, now incorporating a quantum fluctuation term [2, 3, 4]. This enhancement is vital for accurately modeling quantum dipolar droplets [5], [6] in dipolar Bose-Einstein condensates. Originally developed in C, the code has been transitioned to C++ to take advantage of its features. Speedup tests were conducted on two types of GPU cards – one commercial and one HPC-optimized – and the results were compared to earlier versions on GPUs and a modern HPC cluster. Both configurations showed an increase in speed.

Foam injection is a promising alternative for enhanced oil recovery strategies, as it can improve sweep efficiency in gas-assisted processes. Accurately predicting multiphase foam flow in porous media, however, remains challenging, and existing modeling approaches continue to evolve as new foam modeling insights and numerical strategies emerge. In this work, we develop ImpesFOAM, a computational framework for three-phase flow incorporating effects of foam using {Finite volume method} (FVM) formulation within the OpenFOAM framework and {IMplicit-Pressure Explicit-Saturation} (IMPES) method. Two foam formulations are incorporated: an implicit-texture model that captures dry-out, oil effects, and surfactant concentration, and a mechanistic population-balance model describing bubble generation and coalescence. Surfactant transport in the aqueous phase is modeled through an additional conservation equation that includes adsorption effects. The object-oriented high-level programming design of OpenFOAM facilitates the link between mathematical modeling and computational implementation, which makes the developed solver more flexible for further foam model implementations. The solver is validated against a comprehensive set of benchmark cases for two and three-phase flow, with and without foam, designed to isolate gravity, capillary pressure, well-driven flow with source and sink terms, and surfactant transport mechanisms. The results show good agreement with analytical, numerical, and experimental results. Moreover, through two three-dimensional field application cases, we demonstrate that foam-assisted {Enhanced Oil Recovery} (EOR) ultimately yields more economically attractive outcomes, including a substantial increase in oil recovery factor and a decrease in the gas-oil ratio production.

We present moljax, an open-source JAX library for GPU-accelerated method-of-lines simulation of stiff reaction-diffusion PDEs on structured grids. The library combines three capabilities absent from existing Python PDE tools: (i) JIT-compiled adaptive time stepping with accept/reject control flow on GPU; (ii) matrix-free Newton–Krylov solvers using AD for exact Jacobian–vector products; and (iii) FFT/DST/DCT spectral operators with physics-aware preconditioning. Controlled benchmarks on Gray–Scott, Schnakenberg, and Brusselator systems show IMEX and ETDRK4 integrators achieving 10–17 ×  speedup over explicit RK4 on the same GPU and spatial discretization. Work-precision analysis reveals that ETDRK4 is the only method achieving monotone pointwise convergence for pattern-forming systems. A tubular reactor benchmark demonstrates 18–40 ×  speedup over Diffrax using identical spatial discretization. Performance claims are supported by controlled comparisons that hold hardware and spatial discretization fixed where attribution is intended. Code: https://github.com/gogipav14/moljax.

WGrin is a Matlab toolbox aimed at studying Whispering Gallery Modes (WGM) in graded index (GRIN) optical micro-resonators. Such resonators have a dielectric cavity with a spatially varying refractive index. WGrin deals with dielectric cavities the shape of which is a disk or a sphere and the refractive index varies with the radial position. For these two geometries of practical interest, the resonance problem can be formulated in the unique form of a one-dimensional problem in the radial variable. This resonance problem is solved by the Finite Difference Method with Perfectly Matched Layer (PML). WGrin allows the computation of resonance wavelengths and the visualization of WGM in GRIN micro-disk and micro-sphere resonators.

Computational Fluid Dynamics (CFD) of turbulent reacting flows is an essential tool to support the industrial sector transitioning to cleaner and energy efficient technologies. Reynolds-averaged Navier-Stokes (RANS) computations and large eddy simulations (LES) are widely used to accelerate innovation without the need of costly experimental campaigns. While simplified treatment of the chemical processes is often selected for computational cost saving, the integration of detailed chemical kinetics in CFD solvers improves the accuracy of complex phenomena, e.g., pollutant formation pathways, extinction and re-ignition processes. This level of fidelity is essential for designing and developing advanced combustion concepts and low-carbon fuel alternatives. This paper presents the FiReSMOKE solver suite, a collection of finite-rate chemistry solvers for RANS computations and LES of turbulent reacting flows. The suite is implemented in OpenFOAM and leverages OpenSMOKE++ to handle detailed chemistry. Along with the finite-rate models from literature and the availability of a wide range of ordinary differential equation (ODE) solvers, the FiReSMOKE suite includes the novel modal partially-stirred reactor (mPaSR) model, the sample-partitioning adaptive chemistry (SPARC) plug-in, a data-driven methodology for chemistry acceleration, and tabulated adaptive chemistry (TDAC). FiReSMOKE is, to the best of the authors' knowledge, the first OpenFOAM-based suite that offers integrated support for advanced chemistry solvers (SPARC, TDAC), tabulation techniques, and multiple combustion models in a unified and modular framework. This manuscript provides a summary of the theoretical background of the combustion models pertaining to the reactor-based models class and a methodology overview of the solver implementation. The modular design facilitates the integration of new combustion models and numerical techniques, making it adaptable to a wide range of research and engineering applications. Along with details on the numerical implementation of the code, test cases demonstrating the solver capabilities are presented.

We present the new version of FeynGrav, a package that provides tools for working with Feynman rules for gravity models. The new version addresses two principal issues and improves the user experience. Firstly, we present a more sophisticated implementation of the BRST formalism for general relativity and quadratic gravity, producing a finite set of interaction rules between ghosts and gravitons. We also implement a higher-derivative gauge-fixing term for quadratic gravity. Secondly, we implement Feynman rules for Cheung-Remmen variables. These variables present the general relativity action in a polynomial form and produce a finite set of Feynman rules. Lastly, we introduce minor quality-of-life improvements to the package to enhance the user experience.

This paper presents a new software implementation in the form of a PY_GROWTH package, specifically designed for the analysis of models of growth of thin epitaxial films and the corresponding RHEED intensities according to the kinematical approximation. This implementation translates and modernizes legacy C++ simulation algorithms into a highly optimized, testable Python package. PY_GROWTH provides three separate universal engines for solving initial value problem for nonlinear differential equations, any of which can be used depending on the scale of computations.

The discrete dipole approximation (DDA) is a widely used and versatile numerical method for solving electromagnetic scattering by arbitrarily shaped objects. Despite its popularity, quantitative comparisons between independent implementations remain challenging due to differences in linear-system conventions, solver settings, and default numerical parameters. In this work, we introduce a unified software-assisted methodology for cross-verification and benchmarking of three major open-source DDA solvers: DDSCAT, ADDA, and IFDDA. We demonstrate how machine-precision agreement can be achieved across implementations by aligning all free parameters and provide practical equivalence tables enabling reproducible and interoperable simulations. Using this methodology, we perform systematic CPU and GPU performance comparisons covering OpenMP, MPI, and CUDA/OpenCL parallelization. Beyond benchmarking, our approach serves as a practical guide for configuring consistent DDA simulations and for understanding how precision, solver choice, and hardware architecture affect runtime, scalability, and accuracy in computational light-scattering studies. The software package also supports regression testing and bitwise reproducibility verification for future code releases.

FIRE7 is a major update to the FIRE program for integration-by-parts (IBP) reduction of Feynman integrals. A large part of the improvements is related to the automatic reduction and reconstruction with the modular arithmetic approach. The performance of the classical rational polynomial approach is also significantly increased, using an improved presolve algorithm that performs Gaussian elimination to simplify IBP identities before substituting numerical indices as in the Laporta algorithm. Various new command line tools are included to facilitate tasks such as applying an IBP reduction table to reduce a loop integrand as a linear combination of individual integrals.

This manuscript introduces MatSub, an open-access software package designed to facilitate the application of Subgroup Discovery (SGD) algorithms in machine learning and data-driven scientific discovery. A key contribution of MatSub lies in the development of novel quality functions tailored to materials informatics. While existing SGD algorithms with numerical targets often emphasize statistical exceptionality, materials research typically prioritizes the identification of subgroups with extreme or optimal property values. To address this gap, MatSub incorporates quality functions that (1) guide the discovery of subgroups maximizing or minimizing a target property, (2) enforce performance-based boundary constraints to filter out undesired materials, (3) promote orthogonal subgroup discovery to reveal multiple, physically distinct mechanisms affecting material behavior, and (4) enable multitask subgroup discovery to capture subgroups that simultaneously satisfy multiple property requirements. We demonstrate the utility of these quality functions in a case study on segregation energies of single-atom alloy catalysts (SAACs), where MatSub successfully identifies diverse and interpretable subgroups linked to distinct electronic and bonding characteristics. These results highlight the software’s ability to support mechanism-aware analysis and accelerate hypothesis generation in materials science and beyond.