1211 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 proposes Discontinuity-aware Physics-Informed Neural Networks (DPINNs), a novel framework that integrates adaptive Fourier-feature embeddings, a discontinuity-aware Kolmogorov-based architecture, mesh transformation, and learnable artificial viscosity to overcome spectral bias and instability, thereby achieving superior accuracy in solving partial differential equations with sharp discontinuities and complex geometries.
evoxels is a fully Pythonic, differentiable physics framework that unifies 3D microscopy data, physical simulations, and machine learning to enable inverse material design by bridging experimental imaging with predictive modeling for optimizing microstructures and manufacturing routes.
This study establishes the crystallographic origins of ferroelectricity in heterodeformed bilayer hexagonal boron nitride across both small and large twist angles, revealing distinct polarization switching mechanisms involving swirling dislocations and introducing a novel density-functional-theory-informed continuum framework (BFIM) to accurately predict ferroelectric behavior in large-unit-cell heterostructures where traditional methods fail.
This paper demonstrates that the distinctive X-shaped Fermi surface geometry of conducting X-type collinear antiferromagnets, exemplified by , enables highly efficient nonrelativistic charge-spin conversion with up to 90% efficiency and out-of-plane spin generation, offering a superior spin source for low-power spintronic devices compared to existing magnetic materials.
This study extends the investigation of critical collapse in axisymmetric massless complex scalar fields to higher angular modes () using a novel -cartoon symmetry reduction, revealing that while discrete self-similarity and universality persist within each mode, the critical exponents depend explicitly on and extremal black holes are excluded at the threshold.
This paper introduces Martini Mapper, an automated framework that generates transferable Martini 3 coarse-grained models directly from SMILES strings for thousands of diverse molecules, thereby overcoming previous manual mapping limitations and enabling high-throughput molecular simulations.
This paper presents a first-principles computational framework based on the Navier-Stokes-Korteweg equations to simulate surfactant effects in liquid-vapor phase-transforming fluids, successfully demonstrating how surfactants reduce surface tension and influence bubble coalescence and condensation.
This paper proposes the Adaptive Probability Flow Residual Minimization (A-PFRM) method, which reformulates high-dimensional Fokker-Planck equations as first-order Probability Flow ODEs and employs Hutchinson Trace Estimation with adaptive sampling to achieve linear computational complexity and constant wall-clock time while overcoming the curse of dimensionality.
This paper presents a numerical Path Integral Monte Carlo study of bosonic, fermionic, and anyonic fluids in thermal equilibrium on a sphere, investigating how positive curvature influences thermodynamic properties, superfluidity, and particle path dynamics while addressing the sign problem via the restricted path integral method.
This paper introduces radial gausslets, a generalized three-dimensional basis set for atomic systems that retains the computational advantage of diagonal electron-electron interaction terms while achieving high accuracy and compactness in Hartree-Fock and exact diagonalization calculations.