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.
This paper extends the neural network backflow approach to ab-initio solid-state calculations by introducing a scalable two-stage pruning strategy that efficiently manages massive configuration spaces, achieving state-of-the-art accuracy for diverse materials like hydrogen chains, graphene, and silicon while outperforming traditional methods in strongly correlated regimes.
This paper introduces a scalable atomistic electronic potential method that overcomes computational limitations to accurately predict electron-phonon coupling and phonon-limited resistivity in twisted bilayer graphene across large moiré unit cells, revealing a two-order-of-magnitude increase in resistivity as the twist angle decreases from 13.2° to 1.6°.
This paper presents a fully reproducible demonstration of an AI-assisted scientific workflow on canonical physics and mathematics benchmarks, illustrating how contemporary AI can effectively serve as a trustworthy copilot for derivation, implementation, and validation when guided by human oversight and rigorous verification.
This paper demonstrates that a hypothetical network of 175 seafloor pressure sensors, combined with a Bayesian inversion framework utilizing offline precomputation and online data assimilation, can enable real-time probabilistic tsunami forecasting for Cascadia earthquakes with high accuracy and sub-second latency despite sparse offshore observations.
This study demonstrates that while local magnetic moments persist above the Néel temperature in the altermagnets CrSb and MnTe, Kramers' spin degeneracy is smoothly restored as thermal disorder increases, with the transition occurring well below in metallic CrSb due to electronic smearing but only near or above in semiconducting MnTe where the band gap remains stable.
This paper proposes a novel convolutional autoencoder neural ODE (CAE-NODE) framework that successfully constructs a highly compressed, physically consistent latent manifold to accurately predict the full transient dynamics of 2D counterflow flames, including ignition and propagation, with relative errors below 2%.
This paper resolves a conceptual inconsistency in large eddy simulations by proposing a direct approximation of the filtered advection term via a truncated infinite series expansion, which eliminates the need for artificial stabilization and yields improved kinetic energy spectra and velocity correlations compared to classical methods.
This paper presents a stochastic Hamiltonian Monte Carlo-based reconstruction framework for emission tomography that generates image ensembles to quantify uncertainty and distinguish between inverse problem ill-posedness and forward-model inadequacy, offering physically interpretable insights beyond traditional point-estimate methods.
This paper introduces ANNA, a MATLAB and Python toolbox that efficiently computes Newtonian noise for the Einstein Telescope by integrating seismic wave fields defined on 3D finite element meshes, with its accuracy verified against analytical solutions for both full-space and half-space scenarios.
This paper presents an automated, end-to-end "runner" system for stellarator coil optimization that utilizes genetic algorithms and context-aware LLMs to streamline design workflows, features an open-source leaderboard for tracking solutions, and introduces novel in-the-loop finite-element calculations for Von Mises stress analysis.