1258 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.
The paper introduces **peapods**, a high-performance, open-source Python package leveraging a Rust core to efficiently simulate Ising spin systems on periodic Bravais lattices using a comprehensive suite of single-spin-flip, cluster, and replica-based Monte Carlo algorithms validated against exact critical temperatures.
This study demonstrates that incorporating biologically motivated, state-dependent synaptic feedback into a Lipkin-Meshkov-Glick quantum brain model significantly reshapes its phase diagram by expanding the paramagnetic phase and displacing critical boundaries, a phenomenon rigorously characterized through ground-state Husimi distributions, Wehrl entropy, and mean-field dynamical analysis.
This paper introduces SMARL, a reinforcement learning framework that uses enstrophy spectrum-based rewards to develop stable, data-efficient subgrid-scale closures for geophysical turbulence, enabling accurate prediction of extreme events with significantly reduced computational costs.
This paper proposes an improved objective function for the optAPM code, featuring a simplified hotspot cost formulation and pre-interpolation of trail data, to reduce modeling errors and enhance the accuracy of absolute plate motion reconstructions over geological timescales.
This paper proposes a general framework for adaptive sensing of physical dynamical systems, where a trainable attention module learns optimal spatial probing and measurement combination to significantly improve prediction accuracy, effectively reframing neural networks as trainable devices for extracting information from physical systems.
This paper introduces a two-tier extension for the GPU-accelerated micromagnetic framework mumax+ that enables efficient, spatially resolved simulation of multimode cavity magnonics in both ferromagnets and antiferromagnets, successfully validating the tool through eight benchmarks covering phenomena ranging from coherent coupling and mode-selective addressing to dissipative interactions and level attraction.
This paper proposes a simple numerical method based on inverse transform sampling to efficiently load relativistic Maxwellian-type distributions by approximating the cumulative distribution of a shifted-Maxwellian energy distribution with an invertible function to generate random energy and momentum variates.
This paper introduces a discontinuous Galerkin time-domain method that computes Casimir forces by recasting the Maxwell stress tensor into classical scattering problems driven by dipolar excitations, enabling accurate evaluation of interactions across diverse geometries and material properties at finite temperatures.
This paper introduces a Fourier Neural Operator-based surrogate model that rapidly and accurately predicts angle- and energy-resolved proton transport and neutron production in proton therapy, achieving Monte Carlo-level accuracy within seconds to enable real-time adaptive range verification and neutron dose estimation.
This paper introduces a novel modified-gradient (MG) method that employs an implicit projection to reformulate magnetic field gradients, thereby achieving exact divergence-free results with round-off precision in meshless magnetohydrodynamics and demonstrating superior performance over constrained-gradient techniques and the GIZMO code across various test cases.