919 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 study proposes an unsupervised, physics-informed neural network approach that integrates train load data and bridge response with governing differential equations to effectively identify, localize, and quantify damage in steel truss railroad bridges while incorporating prior inspection knowledge.
This paper introduces a stochastic cluster expansion framework that enables near-DMRG accuracy for total correlation energies in large condensed-phase systems by combining exactly treated subspaces with randomly sampled environment orbitals, thereby eliminating the need for prior active space selection and facilitating high-accuracy studies of chemical processes in complex environments.
This paper presents numerical evidence using Clean Numerical Simulation suggesting that the Navier-Stokes equations may admit distinct global solutions arising from initial conditions differing by as little as , thereby challenging the uniqueness of smooth solutions and offering new insights into the associated Millennium Prize Problem.
This paper presents a fault-tolerant quantum algorithmic framework for simulating phase-contrast transmission electron microscopy image formation, which leverages quantum Fourier transforms to efficiently model wave propagation and specimen interactions while offering quantum advantages for specific Fourier-space queries and global statistics despite the measurement overhead required for full image reconstruction.
This paper introduces M-CODE, an open-source, ontology-based categorization system that classifies materials structures by dimensionality, complexity, and evolutionary provenance to support standardized data management and reproducible dataset generation in AI-driven materials science.
This study demonstrates that combining density functional theory with physically informed descriptors enables accurate and chemically transferable machine learning predictions of dopant formation energies in TiO monolayers, even when trained on small, well-curated datasets.
This paper presents a plane-wave implementation of auxiliary-field quantum Monte Carlo within the PAW formalism in VASP that operates at the complete basis set limit with cubic scaling, achieving high-accuracy predictions of lattice constants and bulk moduli for C, BN, BP, and Si by correcting deficiencies in MP2 and RPA methods.
This paper proposes and validates through 3D magnetohydrodynamic simulations that Odd Radio Circles are synchrotron-emitting vortex rings formed by Richtmyer-Meshkov instabilities when shocks interact with fossil radio lobes, a model that successfully reproduces observational constraints and distinguishes itself by not requiring the rings to be centered on host galaxies.
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.
This paper presents a novel open-source immersed fluid-structure interaction framework that synergistically couples the high-performance, GPU-ready MFEM library with the biomechanics-focused FEBio solver to enable robust, scalable simulations of complex biological systems like heart valves.