1221 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.
The paper introduces GPU-MetaD, a full-life-cycle GPU-accelerated metadynamics framework that integrates machine learning potentials to achieve order-of-magnitude performance gains, enabling ab-initio-level rare-event sampling for million-atom systems and revealing a novel size-dependent two-step nucleation mechanism in gallium nitride.
This paper assesses the lattice Boltzmann method as a time-domain numerical approach for electromagnetic wave scattering by validating its accuracy against analytical and semi-analytical solutions across various canonical benchmarks, including 2D and 3D scattering from dielectric cylinders and spheres with different geometries and material contrasts.
This paper presents a comprehensive set of numerical procedures and algorithms for generating various non-Maxwellian velocity distributions, including , kappa, ring, shell, super-Gaussian, and filled-shell distributions, to be used in particle simulations.
This paper presents a reinforcement learning agent utilizing geometric graph representations to efficiently optimize atomic ordering in bimetallic alloy nanoparticles, successfully identifying ground-state structures and demonstrating transferability across compositions and sizes, though with limitations in multi-element systems.
This paper proposes a scalable machine learning framework that derives accurate macroscopic stochastic dynamics for large spatially extended systems by training exclusively on small-system simulations through a partial evolution scheme and hierarchical upsampling, thereby overcoming the prohibitive computational costs of direct large-scale microscopic modeling.
This study analyzes 66 gravitational wave events to demonstrate that the probability of classifying compact objects as neutron stars is highly sensitive to population model assumptions—particularly pairing preferences and spin distributions—rather than just measurement noise or equation of state constraints, leading to significant classification uncertainties for events like GW230529 and GW190425.
This paper presents a reversible, energy-conserving numerical integration scheme for the Sllod equations of motion implemented in LAMMPS, demonstrating that such details are critical for eliminating systematic errors in viscosity calculations and accurately simulating homogeneous flows, particularly at high rates.
This paper introduces Physics-Enhanced Deep Surrogates (PEDS), a data-efficient framework combining a differentiable Fourier solver with a neural network and active learning to accurately and rapidly solve the Phonon Boltzmann Transport Equation across ballistic and diffusive regimes, thereby enabling practical inverse design of nano-scale thermal materials with significantly reduced training data requirements.
This study utilizes direct numerical simulations of isotropic turbulence at Reynolds numbers up to 1300 to demonstrate that the behavior of the second-order Lagrangian velocity structure function is significantly shaped by the limited accessible time scales and the strong, incomplete cancellation between convective and local contributions driven by particle displacements, suggesting that the scaling constant may be sensitive to intermittency while remaining potentially asymptotically constant at even higher Reynolds numbers.
This paper presents a machine learning-driven inverse-design framework that systematically optimizes the boundary shapes of three-dimensional microwave cavities to maximize electromagnetic helicity, identifying robust, high-performance geometries that surpass traditional heuristic design methods.