871 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 BattMo, a flexible MATLAB-based finite volume toolbox for simulating diverse electrochemical battery cells that supports 3D geometries, coupled thermal and degradation models, and FAIR-compliant parameterization while enabling efficient gradient-based optimization through automatic differentiation.
This paper introduces a feature-preserving latent-EnKF that overcomes the Gaussian assumption limitations of traditional ensemble Kalman filters in compressible flows by performing ensemble updates in a learned low-dimensional latent space, thereby accurately recovering shocks and discontinuities without spurious oscillations.
This paper presents an asymptotic analysis and a high-order boundary integral method using Duffy patches to accurately compute the three-dimensional Neumann Green's function for general curved surfaces by decomposing the solution into singular and regular parts, thereby enabling the resolution of open problems in narrow capture theory.
This paper introduces REMAL, a cost-effective active learning framework that employs multitask Gaussian processes to model the joint residual manifold of coupled engineering systems, enabling efficient recovery of equilibrium states for multidisciplinary design analysis without expensive fixed-point iterations.
This paper introduces a computationally efficient beam-membrane biomechanical model of vocal folds that incorporates muscle-driven posturing and glottal conformation to predict voice production dynamics and investigate voice disorders, offering a practical alternative to expensive high-fidelity simulations.
This paper introduces CAVIAR, an electron ptychography framework that achieves sub-Angstrom resolution to reveal spatial correlations in atomic vibrations and accurately determine phonon frequencies from nanoscale volumes, offering a unique tool for studying atom dynamics and developing phonon-based technologies.
This study identifies that the underestimation of the Curie temperature in ferroelectric PbTiO primarily arises from limitations in exchange-correlation functionals rather than machine learning force field inaccuracies, revealing that apparent improvements from short-range models are fortuitous error cancellations while accurate predictions require explicit long-range interactions and improved functionals.
This paper introduces an extensible, open-source SHG Computational Toolkit Suite designed to overcome the limitations of existing analytical models and inaccessible experimental data by providing a coordinated collection of well-documented numerical tools for studying complex, thermally coupled second harmonic generation scenarios.
This paper introduces a Physically Constrained Ensemble Gaussian Process (pc-EGP) framework that integrates physical consistency penalties and ensemble learning to accurately model expensive, heteroskedastic quantum simulations, demonstrating superior performance in predicting critical parameters for the Bose-Hubbard model and optimizing chemical environments for superfluidity compared to conventional methods.
This paper introduces LAPG, a least-action-principle-guided diffusion framework that enhances physical consistency in generative models during inference by combining a conditional score-based model with an action-derived variational prior, thereby enabling reliable extrapolation across time, parameters, and geometries for various physical systems without relying solely on training-time constraints.