physics.comp-ph papers | Gist.Science

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.

Electrohydrodynamic instability of Cu, W and Ti metal nanomelts under radiofrequency E-fields from multiphysics molecular dynamics simulations with coarse-grained density field analysis

This study employs electrodynamics-coupled molecular dynamics simulations and electrocapillary wave theory to investigate the electrohydrodynamic instability and thermal runaway of Cu, Ti, and W nanotips under radiofrequency electric fields, revealing that nanoscale melts exhibit drastically higher viscosities than bulk liquids and identifying critical field parameters that trigger instability regardless of frequency.

Shangyong Wua, Rui Chua, Wenqian Konga, Hongyu Zhanga, Le Shia, Kai Wua, Yonghong Chenga, Guodong Menga, Bing Xiaoa2026-02-16🔬 physics

A T-matrix scattering formalism for electron-beam spectroscopy

This paper presents a versatile T-matrix-based scattering formalism implemented in the open-source `treams_ebeam` software to efficiently simulate cathodoluminescence and electron energy-loss spectroscopy for complex nanophotonic structures interacting with fast electrons.

P. Elli Stamatopoulou, Carsten Rockstuhl2026-02-16🔬 physics

Estimating Full Path Lengths and Kinetics from Partial Path Transition Interface Sampling Simulations

This paper introduces a Markov state model framework that enables the extraction of full path lengths and kinetic properties, such as mean first passage times and rate constants, from the computationally efficient partial paths generated by the replica exchange partial path transition interface sampling (REPPTIS) algorithm.

Wouter Vervust, Elias Wils, Sina Safaei, Daniel T. Zhang, An Ghysels2026-02-16🔬 physics

Neural Quantum States Based on Selected Configurations

This paper demonstrates that the Neural Quantum States-based Selected Configuration (NQS-SC) approach significantly outperforms the traditional Variational Monte Carlo (NQS-VMC) method in accuracy, efficiency, and systematic improvability for electronic ground-state calculations, particularly for statically correlated systems, though both methods still struggle with dynamical correlation.

Marco Julian Solanki, Lexin Ding, Markus Reiher2026-02-16🔬 cond-mat

Enhanced numerical approaches for modeling insoluble surfactants in two-phase flows with the diffuse-interface method

This paper proposes two simple and practical numerical enhancements for modeling insoluble surfactants in two-phase flows using the diffuse-interface method, which improve accuracy without increasing computational cost and include a new challenging benchmark case for future method evaluation.

Shu Yamashita, Shintaro Matsushita, Tetsuya Suekane2026-02-16🔬 physics

Tensor Network Compression for Fully Spectral Vlasov-Poisson Simulation

This paper introduces a novel numerical method for kinetic plasma simulation that combines Strang splitting with fully spectral Vlasov-Poisson solvers by representing the phase-space distribution function and operators as adaptive low-rank tensor networks, thereby enabling efficient time stepping and self-consistent field calculations directly in compressed form without reconstructing the full phase-space grid.

Erik M. Åsgrim, Luca Pennati, Marco Pasquale, Stefano Markidis2026-02-16🔬 physics

Aging following a zero-temperature quench in the $d=3$ Ising model

Through large-scale Monte Carlo simulations of the $d=3$ Ising model following a zero-temperature quench, this study resolves previous discrepancies by finding that the autocorrelation exponent $\lambda \approx 1.58$ is consistent with the theoretical lower bound of $d/2$ , thereby refuting claims of universality violation below the roughening transition.

Denis Gessert, Henrik Christiansen, Wolfhard Janke2026-02-13🔬 cond-mat

Physics-Informed Neural Network for Elastic Wave-Mode Separation

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.

E. A. B. Alves, P. D. S. de Lima, D. H. G. Duarte, M. S. Ferreira, J. M. de Araújo, C. G. Bezerra2026-02-13🔬 physics

Simulation of Muon-induced Backgrounds for the Colorado Underground Research Institute (CURIE)

This paper presents a comprehensive Monte Carlo simulation framework using coupled \textsc{mute} and \textsc{geant4} tools to characterize muon-induced neutron backgrounds at the shallow-underground CURIE facility, providing site-specific flux predictions and a validated depth-intensity relation to guide experimental design for low-background physics.

Dakota K. Keblbeck, Eric Mayotte, Uwe Greife, Kyle G. Leach, Wouter Van De Pontseele, Caitlyn Stone-Whitehead, Luke Wanner, Grace Wagner2026-02-13⚛️ hep-ex

PINNs for Electromagnetic Wave Propagation

This study presents a hybrid Physics-Informed Neural Network (PINN) framework that overcomes traditional accuracy and energy conservation limitations in electromagnetic wave propagation by integrating time-marching, causality-aware weighting, interface continuity losses, and a Poynting-based regularizer, thereby achieving FDTD-comparable performance without labeled training data.

Nilufer K. Bulut2026-02-13🤖 cs.AI

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