🔬 Category

physics.comp-ph

903 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.

Microcanonical simulated annealing: Massively parallel Monte Carlo simulations with sporadic random-number generation

This contribution presents a general microchannel simulated annealing procedure (MicSA) that drastically reduces the computational cost of random number generation in massively parallel Monte Carlo simulations, and demonstrates its effectiveness and dynamic equivalence to standard methods through rigorous benchmarks on three-dimensional Ising spin glasses using GPUs and the Janus-II supercomputer.

M. Bernaschi, C. Chilin, L. A. Fernandez, I. González-Adalid Pemartín, E. Marinari, V. Martin-Mayor, G. Parisi, F. Ricci-Tersenghi, J. J. Ruiz-Lorenzo, D. Yllanes2026-05-07🔬 physics

Maxwell à la Helmholtz: Direct boundary integral equations for 3D scattering by perfect electric conductors via Helmholtz operators

This contribution presents clearly solvable direct formulations of second-kind boundary integral equations for three-dimensional electromagnetic scattering by perfect electric conductors, which are derived using Helmholtz operators with tailored function spaces and charge-conserving modifications for low-frequency stabilization and validated through high-order numerical experiments.

Carlos Pérez-Arancibia, Catalin Turc2026-05-07🔢 math

Interaction-controlled localization in one-dimensional chain: From edges to domain walls

Using a Hartree-Fock mean-field approach, this study demonstrates that in a half-filled Su-Schrieffer-Heeger chain, the localization of bound states is governed by the ratio of extended to on-site Hubbard interactions (2V/U2V/U), which determines whether edge spin-density-wave modes or mid-chain charge-density-wave domain walls emerge, independent of the system's band topology.

Rahul Samanta, Sudin Ganguly, Santanu K. Maiti2026-05-07🔬 cond-mat.mes-hall

Efficient Deconvolution in Populational Inverse Problems

This paper proposes an efficient methodology for solving populational inverse problems by simultaneously deconvolving unknown noise distributions and inferring parameter distributions from multiple physical system observations, utilizing a modified gradient descent algorithm and an active learning scheme to accelerate computation and enable automatic differentiation of black-box models.

Arnaud Vadeboncoeur, Mark Girolami, Andrew M. Stuart2026-05-06📊 stat

Integration of Silica in G4CMP for Phonon Simulations: Framework and Tools for Material Integration

This paper presents a new formalism and Python-based tools within the G4CMP framework to enable phonon simulations in custom materials, demonstrated through a detailed analysis of silica phonon transport properties for BeEST-style superconducting detector experiments.

Caitlyn Stone-Whitehead, Israel Hernandez, Connor Bray, Allison Davenport, Spencer Fretwell, Abigail Gillespie, Joren Husic, Mingyu Li, Andrew Marino, Kyle Leach, Bismah Rizwan, Wouter Van De Pontseel (…)2026-05-06🔬 physics

HINORA II: Testing the Existence of the Council of Giants in ΛCDM simulations

This work applies the HINORA algorithm to cosmological simulations and finds that the presence of the Giant Ring constitutes a statistically rare anomaly (over 2.7 sigma) in the standard Λ\LambdaCDM model, suggesting it could either be a random configuration or evidence of physical processes not captured by pure dark matter simulations.

Edward Olex, Alexander Knebe, Noam I. Libeskind, Stefan Gottlöber, Dmitry I. Makarov2026-05-06🔭 astro-ph

Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques

This paper proposes a physics-informed machine learning framework that predicts the Euler characteristic of input images by training neural networks to generate unit vector fields (interpreted as spin configurations) and computing their skyrmion number, utilizing a magnetic Hamiltonian as a loss function to constrain degrees of freedom without requiring large pre-existing datasets.

Gyunghun Yu (Department of Physics, Kyung Hee University, Seoul, South Korea), Seong Min Park (Department of Physics, Kyung Hee University, Seoul, South Korea), Han Gyu Yoon (Department of Physics, Ky (…)2026-05-06🤖 cs.LG