Computer Physics Communications

We introduce Merlino2D, an efficient fluid plasma solver (available at https://github.com/PTL-Unibo/Merlino2D) for the simulation of low-temperature gas discharges and plasma-based devices. The solver operates on structured and unstructured triangular meshes and supports robust two-dimensional simulations of stationary and time-dependent regimes. Merlino2D integrates the open-source LisbOn KInetics Boltzmann solver (LoKI-B), which computes the electron energy distribution function and associated swarm parameters. The time-dependent drift–diffusion equations coupled with Poisson’s equation are formulated as a monolithic system of differential-algebraic equations (DAEs). The system of equations is integrated in time with a fully implicit time integration scheme with adaptive time stepping and error control. This approach results in efficient handling of numerical stiffness and favorable scaling of the computational time with mesh size. In this work, the numerical methodology underlying Merlino2D is described, and the numerical accuracy of the transport schemes is verified against analytical solutions. In addition, the solver is applied to two-dimensional simulations of both DC and AC discharges, including corona discharges and surface dielectric barrier discharges (DBDs), demonstrating its capability to reproduce experimental trends and results reported in the literature.

We present EEXICC, a Monte Carlo event generator designed to simulate the production of doubly heavy baryons (Ξ_{cc}, Ξ_{bc}, and Ξ_{bb}) via e+e− annihilation. Based on nonrelativistic QCD effective theory, the generator calculates the process e+ + e− → Ξ_{QQ′} + \bar{Q}′+\bar{Q} using an improved trace technique at the amplitude level, which greatly improves numerical efficiency compared with traditional squared-amplitude methods. EEXICC uses a modular Fortran implementation for hard-process generation and a C++ interface to the bundled PYTHIA 8.317 library, enabling convenient integration into complete event simulation workflows. The program supports both weighted and unweighted event generation, and its numerical reliability has been verified against existing theoretical results. EEXICC provides a flexible and robust tool for studying the properties of doubly heavy baryons at future high-luminosity and high-energy e+e− colliders such as the CEPC and FCC-ee.

High-fidelity electromagnetic scattering computation for nonspherical and internally inhomogeneous atmospheric particles is a key link between particle microphysics and observables in measurement modeling and remote-sensing forward simulation. In practice, however, the use of high-fidelity solvers is often hindered by limited openness, insufficient modularity for large-scale parameter sweeps, high computational cost for multi-orientation reuse, and weak integration with measurement-oriented workflows. We present TransitionMatrices.jl, an open-source Julia package centered on the Invariant Imbedding T-Matrix (IITM) method. The framework provides a modular pipeline covering particle modeling, IITM-based T-matrix computation, and region-wise heterogeneity partitioning acceleration: the inscribed-sphere region is treated as a homogeneous core domain and accelerated using a logarithmic-derivative-based Mie scattering method, while the region between the inscribed and circumscribed spheres is treated as an extended inhomogeneous domain with particle-shape-dependent acceleration strategies. The package further supports validation-oriented Extended Boundary Condition Method (EBCM) cross-checking, far-field scattering products (cross sections, amplitude matrix, scattering/Mueller matrix), and coordinate transformations for efficient orientation handling. We report systematic credibility checks, including Mie consistency in spherical cases, IITM–EBCM cross-validation where applicable, and rotation-related error assessment. Finally, we demonstrate WRF-coupled polarimetric forward simulations to illustrate how particle-shape assumptions propagate into simulated atmospheric observables.

qcombo is a Python package for the symbolic evaluation of commutators between general quantum many-body operators expressed in normal-ordered form using the generalized Wick theorem. The package provides an automated and systematic framework for generating the corresponding algebraic expressions, significantly reducing the risk of human error in lengthy and complex analytical derivations. It is designed to assist the development and implementation of modern many-body methods in nuclear physics, quantum chemistry, and related fields. The functionality and workflow of the package are demonstrated through an application to the in-medium similarity renormalization group (IMSRG) method, which has been widely used for nuclear ab initio calculations. As a representative example, qcombo is employed to automatically generate the complete set of multi-reference IMSRG flow equations with operators truncated at the normal-ordered three-body level.

Doppler Broadening (DB) of annihilation radiation is a well-established technique within Positron Annihilation Spectroscopy (PAS), used for probing the electronic structure of materials. The analysis of DB experimental data relies on gamma spectroscopy analysis tools, while depth profiling using variable-energy slow positron beams depends on solving the positron diffusion equation. Traditional Variable Energy Doppler Broadening (VEDB) analysis tools, such as VEPFIT and ROYPROF, often present limitations due to outdated interfaces and lack of integration with spectroscopy analysis platforms. To address these limitations, this work introduces SciPAS, an open-source Python package for PAS analysis. SciPAS offers functionalities including Coincidence Doppler Broadening (CDB) filtering, two-dimensional CDB analysis with DB and resolution extraction, and computation of line-shape parameters (S and W). Furthermore, it integrates modules for generating thermal positron implantation profiles based on established models, solving positron diffusion equations using finite-difference methods and optimizing diffusion length. This work presents the architecture of the SciPAS package and the validation results and demonstrates the application of the package through case studies.

Nonequilibrium Green’s functions provide a powerful framework for studying quantum many-body dynamics including the laser-induced dynamics in solids. The Non-Equilibrium Systems Simulation package (NESSi) offers an efficient platform for such simulations, ranging from perturbative approaches like nonequilibrium GW to nonequilibrium dynamical mean-field theory. However, simulations based on nonequilibrium Green’s functions become computationally demanding when the dynamics span a large temporal range, such as from sub-femtosecond electron dynamics to the picosecond dynamics of collective modes. Due to the memory integral in the Kadanoff-Baym equations, which serve as equations of motion for nonequilibrium Green’s functions, the computational cost scales as O(N_t^3) with the number of timesteps N_t, and the memory requirement scales as O(N_t^2). In this work, we extend NESSi by incorporating techniques that aim to overcome this bottleneck: (i) By truncating the memory integrals in the KBE to a maximum of N_c timesteps, the computational complexity is reduced to O(N_t N_c^2), and the memory requirement to O(N_c^2). Provided that the results converge with respect to the cutoff Nc, memory truncation allows to extend the simulations to significantly longer times. (ii) We introduce functionalities to describe nonequilibrium steady states, i.e. time-translationally invariant nonequilibrium states. Such states are relevant for transport settings, and they provide an approximate description of slowly evolving (prethermal) nonequilibrium states.

Linear systems with finite-group symmetry, from geometric invariances to repetitive structural patterns, are ubiquitous in science and engineering, and standard computational software rarely exploits that structure. This paper introduces PySymmetry, an open-source Sage/Python framework that applies the constructive algorithms of classical representation theory to the symmetry reduction of G-equivariant linear systems. Projection operators built from the characters of the irreducible representations of a finite group generate a symmetry-adapted basis, which carries an equivariant operator into block-diagonal form and decouples one large problem into several small independent ones. A single character-theoretic construction serves two paths inside one open environment: an exact symbolic path over an exact ring, and a numerical path interoperable with NumPy, SciPy, and sparse matrix formats. Three case studies exercise both. The GF method of molecular vibrations illustrates the workflow. A non-Hermitian Schrödinger eigenvalue benchmark shows the reduced path to be nearly 17 times faster than a general-purpose dense eigensolver on the largest grid tested. A symbolic investigation produced the first complete analytical classification of the nested cube central configuration in celestial mechanics. Source code, documentation, and the notebooks that reproduce the results are publicly available.

This paper introduces a software suite whose purpose is simulating the linear momentum transfer to charged particles caused by interactions with an arbitrary superposition of Gaussian beams or plane wave beams. The program can automatically compare the results of the momentum transfer between the relativistic Lorentz force model and the relativistic ponderomotive approximation method, then create 2D momentum transfer maps for parameter sweeps such as increasing the laser intensity, together with 2D error maps, for quantitatively finding the validity domain of the ponderomotive method. The implementation is a hybrid C and OpenMP for the simulation segments, and Python scripts for easily configuring the simulation scenarios and creating parameter sweeps. The core particle pusher algorithm for the electromagnetic mode is the Higuera-Cary push, alongside a fourth-order Runge-Kutta integrator for the ponderomotive method.

CN-Waves’ objective is to simplify and automate the generation and modeling of realistic water wave fields in numerical wave tanks and open domains. Wave modeling uses the High-Order Spectral codes HOS-Ocean and HOS-NWT, which are based on fully nonlinear potential flow theory. CN-Waves packages the HOS command-line executable files, and provides a Python package to execute HOS simulations. The Python methods have default numerical parameters that allow a user with only a basic knowledge of wave theory to model and generate water waves, while allowing a more advanced user to control the different parameters. Various visualization features have been implemented. The package is designed to facilitate the launch and analysis of a substantial number of simulations. Furthermore, the Python package implements several wave generation methodologies for the wave tank configuration. These include well-established approaches such as second-order wave generation or accounting for the side-walls reflection during the generation of oblique waves. Other recently developed methods, including the time reversal method, an iterative approach to calibrate a sea state, and the generation of response-conditioned waves, are also made available in CN-Waves.

We present a finite element (FE) software library, IRENE, which allows for numerically solving the dynamics of a viscous fluid layer embedded in three-dimensional space. IRENE describes two-dimensional, dynamical open surfaces, and it is restricted to manifolds described with the Monge gauge. IRENE supports a wide range of boundary conditions and shapes of inter-surface obstacles, and is built upon the user-friendly and versatile finite element computational software (FEniCS). Also, the library can describe a wide range of physical regimes—both low-Reynolds-number and inertia-dominated ones—capturing the complex coupling between in-plane flows, out-of-plane deformations, surface tension, and elastic response. We validate IRENE against known analytical and numerical results, and demonstrate its capabilities through physical examples. Overall, IRENE provides a versatile and efficient tool for understanding fluid-layer dynamics on multiple physical scales, from flows of lipidic membranes on a microscopic level, to fluid flows on a macroscopic scale, to atmospheric air flows on a planetary level.