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 introduces Discrete Solution Operator Learning (DiSOL), a novel paradigm that learns discrete, procedure-based solver stages to accurately and stably solve partial differential equations across varying and topologically complex geometries, addressing the limitations of traditional continuous function-space operator learning in engineering settings.
This paper introduces \textbf{warpax}, a GPU-accelerated Python toolkit that employs continuous gradient-based optimization and automatic differentiation to perform observer-robust energy condition analysis of warp drive spacetimes, revealing that traditional single-frame evaluations significantly underestimate both the spatial extent and magnitude of energy violations.
This paper introduces GET-SEI, a general framework combining graph contrastive learning, extended dynamic mode decomposition, and transition path theory to automatically characterize local atomic environments and quantify lithium transport kinetics and pathways across diverse solid-state electrolyte/lithium metal interfaces for targeted SEI engineering.
This paper refutes the conclusions of Savard et al. regarding the necessity of high particle counts in implicit particle-in-cell schemes by demonstrating that procedural diagnostic errors in their original study led to misleading results, which are corrected to show that their claims do not hold under independent scrutiny.
This paper presents a novel finite element formulation for incompressible viscous flow that directly minimizes the L2 norm of the pressure gradient using Q9 elements, thereby eliminating pressure degrees of freedom while delivering stable, oscillation-free solutions, built-in error indicators, and viscosity estimation capabilities without requiring stabilization techniques.
This paper introduces a unified, software-assisted methodology to achieve machine-precision cross-verification and fair performance benchmarking across three major open-source DDA solvers (DDSCAT, ADDA, and IFDDA) by aligning numerical parameters and providing equivalence tables for reproducible, interoperable electromagnetic scattering simulations.
This paper investigates geometric regularization strategies for autoencoder-based reduced-order models with neural ODE dynamics, finding that while near-isometry, stochastic gain, and curvature penalties often hinder long-horizon latent dynamics training despite improving local smoothness, Stiefel projection of the first decoder layer consistently enhances conditioning and rollout performance by better addressing latent-geometry mismatch.
This paper advocates for a more rigorous test problem for viscous hydrodynamics codes by introducing a nonuniform-density Gaussian velocity shear scenario that exposes errors in the calculation of viscous stress tensors, fluxes, and source terms, while also providing a detailed exposition of the Navier-Stokes equations in various coordinate systems.
This paper presents a neural network-based method that projects metastable basin data into a low-dimensional manifold with a preassigned distribution to identify efficient collective variables, enabling reduced-dimensional sampling and clear reaction free energy profiles for both two-state and multi-step chemical processes.
This paper introduces a Bayesian Langevin approach to simultaneously quantify deterministic and stochastic dynamics in critical transitions, applying it to the 1996 North American blackout to reveal that a permanent grid state change occurred two minutes before the official trigger, thereby highlighting the necessity of distinguishing destabilizing factors for reliable early warning.