1259 papers
Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.
On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.
Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.
This paper presents a novel open-source immersed fluid-structure interaction framework that synergistically couples the high-performance, GPU-ready MFEM library with the biomechanics-focused FEBio solver to enable robust, scalable simulations of complex biological systems like heart valves.
This study investigates how five different finite-difference schemes and mesh geometry affect shock wave resolution in two-dimensional supersonic inviscid flows, identifying that while various methods introduce non-physical perturbations, specific AUSM+ formulations combined with techniques like flux limiting and artificial dissipation offer superior robustness and accuracy validated against experimental data.
This study evaluates three gradient reconstruction schemes and various limiter functions within a cell-centered finite volume framework for solving compressible RANS equations on unstructured grids, demonstrating that sophisticated formulations are essential for stability and accuracy in complex geometries while a novel CFL-based convergence acceleration technique effectively drives residuals to machine zero.
This study evaluates the performance of three limiter formulations (Venkatakrishnan, Wang's modification, and Nishikawa's R3) within a second-order finite volume scheme for simulating steady, turbulent transonic flows over a NACA 0012 airfoil, finding that all yield comparable results consistent with experimental data when their control constants are appropriately tuned, despite exhibiting different dissipative characteristics.
The paper introduces **aurel**, an open-source Python package that streamlines both symbolic and numerical relativistic calculations by leveraging a flexible caching system and finite-difference methods to process data from analytical expressions or Numerical Relativity simulations.
This paper presents a stochastic framework for modeling low-population dynamics in circulating-fuel reactors under perfect mixing, deriving an Itô SDE system and Monte Carlo solver that validate mean behaviors against deterministic solutions while revealing specific limitations in capturing delayed-neutron precursor variances and reactivity loss biases.
This paper demonstrates that Physics-Informed Neural Networks (PINNs) can accurately solve the many-body Schrödinger equation for the deuteron ground state using realistic nucleon-nucleon interactions, achieving a relative binding energy error of approximately compared to established numerical benchmarks.
This study demonstrates that accurately capturing both laminar and turbulent regions over a wing using Wall-Modeled Large-Eddy Simulation (WMLES) requires a hybrid grid resolution strategy combined with the introduction of unsteady disturbances to trigger transition, as standard uniform grids fail to satisfy the conflicting near-wall resolution requirements of both flow regimes.
This paper proposes and evaluates three adaptive non-intrusive reduced-order modeling frameworks—Adaptive OpInf, Adaptive NiTROM, and a hybrid approach—that dynamically update latent subspaces and reduced dynamics online to overcome the limitations of static surrogates, demonstrating that these self-correcting models significantly improve robustness and energy tracking in evolving flow regimes while advocating for cost-aware predictive reporting.
This study utilizes classical molecular dynamics simulations of argon-based two- and three-region models to analyze the intermediate fluctuations, correlations, and temperature distributions that characterize the process of heat conduction leading to thermal equilibrium, thereby providing a detailed microscopic perspective on the Zeroth Law of Thermodynamics.