1191 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 presents a multiphysics finite element framework that integrates exact surface roughness representations with mechanical contact and electrostatic simulations to accurately predict the performance of triboelectric nanogenerators, offering superior agreement with experimental data compared to traditional analytical models.
This paper proposes a principal component analysis framework for non-equilibrium quantum dynamics that maximizes information in the largest principal component to link dimensionality reduction with physical observables, demonstrated through Heisenberg spin chain simulations and applicable to higher-dimensional quantum simulators.
This paper proposes a quantum algorithm utilizing efficient block-encoding to simulate light propagation through weakly inhomogeneous media by mapping the paraxial wave equation to a time-dependent Schrödinger equation, demonstrated through the modeling of spherical aberrations in a 1D Gaussian beam passing through a finite-thickness lens.
This paper introduces a highly efficient and accurate machine learning surrogate model based on a trimmed SchNet architecture to predict many-body dispersion interactions in polymer melts, enabling their practical incorporation into large-scale molecular simulations while capturing key physical features and generalizing across diverse polymer systems.
This paper introduces a novel, universal variance reduction method called Generalized Antithetic Sampling for floating random walk-based capacitance extraction that is simple, efficient, and provably reduces variance across all scenarios, achieving up to a 50% reduction in required random walks and extraction time compared to existing approaches.
This paper presents a locally conservative coupled Continuous-Discontinuous Galerkin discretization of the shallow water equations integrated into the ADCIRC model to accurately simulate compound floods by accounting for the complex interactions between storm surge and rainfall-induced runoff, as validated through laboratory tests and Hurricane Harvey simulations.
PyMieDiff is a fully differentiable, GPU-compatible PyTorch library that implements Mie scattering for layered spherical particles with native autograd support for spherical Bessel and Hankel functions, enabling seamless integration with gradient-based optimization and physics-informed neural networks.
This memorial review article honors Wolfgang Helfrich by exploring how continuum elastic theories and liquid crystal principles govern the diverse morphologies of biomembranes, revealing that shapes like spheres, cylinders, and tori form a geometric group independent of specific membrane equations.
This paper demonstrates that by constructing kinetic closures around non-Maxwellian reference states with finite fourth moments, a pressure-gradient-driven conductive heat flux emerges in the hydrodynamic regime, a phenomenon absent in standard Maxwellian-based theories.
This paper introduces a sparse convex-relaxation framework using a Müntz–Szász/Jacobi dictionary to estimate effective local scaling exponents from short, boundary-anchored velocity profiles, demonstrating its utility as a finite-scale geometric diagnostic that reveals directional anisotropy and vorticity-aligned roughness structures in turbulent flows without requiring external calibration.