922 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 proposes and validates through 3D magnetohydrodynamic simulations that Odd Radio Circles are synchrotron-emitting vortex rings formed by Richtmyer-Meshkov instabilities when shocks interact with fossil radio lobes, a model that successfully reproduces observational constraints and distinguishes itself by not requiring the rings to be centered on host galaxies.
This paper proposes a computationally efficient Physics-Informed Neural Network (PINN) that solves a scalar Poisson equation to accurately separate P and S elastic wave modes in both homogeneous and non-homogeneous media, outperforming traditional methods by reducing transverse wave leakage.
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.
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 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.
This paper develops an analytical framework to evaluate stochastic Verlet-type integrators by deriving closed-form expressions for their accuracy in simulating diffusion, drift, and harmonic potential sampling, ultimately identifying the GJ set of integrators as the most robust option for high-quality thermodynamic simulations.
This paper introduces a model of Dirac-Bianconi driven oscillators that couple topological signals on nodes and links within higher-order networks, utilizing phase reduction to analyze their synchronization dynamics beyond traditional node-based paradigms.
This paper evaluates the EUCLID framework for the automated discovery of hyperelastic constitutive laws using experimental data from natural rubber specimens, comparing its performance against conventional parameter identification methods in terms of predictive accuracy, generalization to unseen geometries, and coverage of the material state space.