1164 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 self-adjusting FEM-BEM coupling scheme for the nonlinear Poisson-Boltzmann equation that automatically determines an optimal relaxation parameter using a Newton-Raphson method with a cubic approximation, achieving a 1.37x speed-up and eliminating the need for trial-and-error parameter tuning in highly charged molecular systems.
This paper introduces a method that combines transition interface sampling simulations conditioned on different collective variables using Multistate Bennett Acceptance Ratio (MBAR) reweighting to significantly improve statistical accuracy in path ensemble calculations, as demonstrated on both simple and complex molecular systems.
This numerical study of a one-dimensional long-range -spin glass model reveals that strong finite-size effects and closely spaced transition temperatures obscure the expected one-step replica symmetry breaking, suggesting instead a direct transition to a full replica symmetry broken phase and implying that the Kauzmann temperature in three dimensions may be zero.
This paper presents a shifted interface method for simulating coupled transient poroelasticity with embedded cracks, demonstrating through various test cases that this approach effectively handles complex internal discontinuities on non-body-fitted meshes while achieving first-order convergence away from crack tips.
This paper proposes a fractal geometry-governed diffusion-reaction model to explain differential tissue responses to FLASH ultra-high dose rate irradiation, demonstrating that structural heterogeneity and anomalous subdiffusive dynamics significantly suppress long-range oxygen transport and create isolated reactive domains compared to classical Euclidean diffusion.
This paper presents an interpretable, structure-preserving graph neural solver that integrates classical numerical principles with graph neural networks to achieve stable, accurate, and computationally efficient long-horizon predictions for parametric hyperbolic conservation laws while inherently respecting physical properties like local conservation and upwinding.
This study introduces a scalable probabilistic workflow that integrates Bayesian correction and deep learning surrogates to bridge image-based fracture geometries with uncertainty-aware hydraulic predictions, effectively capturing the impact of aperture heterogeneity and 3D void complexity on transmissivity while avoiding the computational cost of repeated high-fidelity simulations.
This paper establishes spectral design principles for enhancing local-excitation retention in impurity-assisted atomic arrays by utilizing biorthogonal eigenmode decomposition to reveal that survival dynamics depend on both eigenmode decay rates and initial-state overlaps, leading to a surrogate objective that successfully guides the inverse design of nontrivial aperiodic atomic configurations.
This paper introduces a physically motivated, learnable basis transformation that enhances the accuracy of Neural-Network Variational Monte Carlo (NNVMC) by reshaping the target ground state into a more representable form, thereby improving performance for architectures like FermiNet and message-passing networks without increasing model complexity.