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In this paper, we describe a new hydrodynamics code for 1D and 2D astrophysical simulations, BETHE-hydro, that uses time-dependent, arbitrary, unstructured grids. The core of the hydrodynamics algorithm is an arbitrary Lagrangian-Eulerian…

Astrophysics · Physics 2009-11-13 Jeremiah W. Murphy , Adam Burrows

Relativistic macroscopic plasma dynamics can be described by general-relativistic magnetohydrodynamics. In many high-energy astrophysical settings, such as the interior dynamics of magnetized stars, the ideal GRMHD approximation, in which…

High Energy Astrophysical Phenomena · Physics 2025-10-23 Sara Azizi , Swapnil Shankar , Philipp Mösta , Roland Haas , Erik Schnetter

We present a new approach for stably evolving general relativistic magnetohydrodynamic (GRMHD) simulations in regions where the magnetization $\sigma=b^2/\rho c^2$ becomes large. GRMHD codes typically struggle to evolve plasma above…

High Energy Astrophysical Phenomena · Physics 2024-07-16 Andrew Chael

With recent advancements in machine learning for interatomic potentials, Python has become the go-to programming language for exploring new ideas. While machine-learning potentials are often developed in Python-based frameworks, existing…

High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality modeling and computer animation in online games or real-time Virtual Reality (VR) systems. However, the large-scale simulation of incompressible…

Graphics · Computer Science 2023-05-08 Jin Li , Yang Gao , Ju Dai , Shuai Li , Aimin Hao , Hong Qin

Multiscale optimization is an attractive research field recently. For the most of optimization tools, design parameters should be updated during a close loop. Therefore, a simple Python code is programmed to obtain effective properties of…

Computational Engineering, Finance, and Science · Computer Science 2017-03-14 Fan Ye , Hu Wang

Multiphysics modeling of liquid metal fusion blankets, which produce tritium and convert energy of neutrons created via fusion reactions into heat, is crucial for predicting performance, ensuring structural integrity, and optimizing energy…

We present GRaM-X (General Relativistic accelerated Magnetohydrodynamics on AMReX), a new GPU-accelerated dynamical-spacetime general relativistic magnetohydrodynamics (GRMHD) code which extends the GRMHD capability of Einstein Toolkit to…

Instrumentation and Methods for Astrophysics · Physics 2025-12-23 Swapnil Shankar , Philipp Mösta , Steven R. Brandt , Roland Haas , Erik Schnetter , Yannick de Graaf

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods…

Optimizing the performance of computational fluid dynamics (CFD) applications accelerated by graphics processing units (GPUs) is crucial for efficient simulations. In this study, we employed a machine learning-based autotuning technique to…

Performance · Computer Science 2024-02-21 Weicheng Xue , Christohper John Roy

We present a reduced magnetohydrodynamic (MHD) mathematical model describing the dynamical behavior of highly conducting plasmas with frozen-in magnetic fields, constrained by the assumption that, there exists a frame of reference, where…

Plasma Physics · Physics 2022-02-23 Igor V Sokolov , Lulu Zhao , Tamas I Gombosi

We present Arepo-MCRT, a novel Monte Carlo radiative transfer (MCRT) radiation-hydrodynamics (RHD) solver for the unstructured moving-mesh code Arepo. Our method is designed for general multiple scattering problems in both optically thin…

Astrophysics of Galaxies · Physics 2020-12-16 Aaron Smith , Rahul Kannan , Benny T. -H. Tsang , Mark Vogelsberger , Ruediger Pakmor

In this paper we describe the implementation of our C++ resistive magnetohydrodynamics solver. The framework developed facilitates the separation of the code implementing the specific numerical method and the physical model, on the one…

Numerical Analysis · Computer Science 2017-01-06 Lukas Einkemmer

We present the new general-relativistic magnetohydrodynamics (GRMHD) capabilities of the Einstein Toolkit, an open-source community-driven numerical relativity and computational relativistic astrophysics code. The GRMHD extension of the…

We describe a strategy for code modernisation of Gadget, a widely used community code for computational astrophysics. The focus of this work is on node-level performance optimisation, targeting current multi/many-core IntelR architectures.…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-09-27 Fabio Baruffa , Luigi Iapichino , Nicolay J. Hammer , Vasileios Karakasis

Disc winds play a crucial role in many accreting astrophysical systems across all scales. In accreting white dwarfs (AWDs) and active galactic nuclei (AGN), radiation pressure on spectral lines is a promising wind-driving mechanism.…

High Energy Astrophysical Phenomena · Physics 2025-07-08 Amin Mosallanezhad , Christian Knigge , Nicolas Scepi , James H. Matthews , Knox S. Long , Stuart A. Sim , Austen Wallis

We introduce a pressure robust Finite Element Method for the linearized Magnetohydrodynamics equations in three space dimensions, which is provably quasi-robust also in the presence of high fluid and magnetic Reynolds numbers. The proposed…

Numerical Analysis · Mathematics 2024-01-03 L. Beirão da Veiga , F. Dassi , G. Vacca

In many astrophysical plasmas, the Coulomb collision is insufficient to maintain an isotropic temperature, and the system is driven to the anisotropic regime. In this case, magnetohydrodynamic (MHD) models with anisotropic pressure are…

Plasma Physics · Physics 2023-08-09 Hongyang Luo , John G. Lyon , Binzheng Zhang

We present a finite-volume, genuinely 4th-order accurate numerical method for solving the equations of resistive relativistic magnetohydrodynamics (Res-RMHD) in Cartesian coordinates. In our formulation, the magnetic field is evolved in…

High Energy Astrophysical Phenomena · Physics 2024-07-12 Andrea Mignone , Vittoria Berta , Marco Rossazza , Matteo Bugli , Giancarlo Mattia , Luca Del Zanna , Lorenzo Pareschi

Partial Differential Equations (PDEs) are fundamental for modeling physical systems, yet solving them in a generic and efficient manner using machine learning-based approaches remains challenging due to limited multi-input and multi-scale…

Machine Learning · Computer Science 2025-08-12 Yichen Luo , Jia Wang , Dapeng Lan , Yu Liu , Zhibo Pang