1197 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 and validates a novel two-level fluid-kinetic coupling method that combines Direct Simulation Monte Carlo (DSMC) for near-wall layers with a high-order Lattice-Boltzmann (HOLB) scheme for bulk flow, enabling the first computationally feasible simulation of coherent structure regeneration cycles and transition to turbulence in wall-bounded flows at Reynolds numbers up to thousands.
This paper introduces Fermionic Antisymmetric Spatio-Temporal Network, a neural network framework that treats time as an explicit input to solve the real-space time-dependent Schrödinger equation for many-electron systems via global optimization, achieving high accuracy in simulating coherent multi-electron dynamics across various dimensions and interaction regimes.
This paper employs the Hartree mean-field approximation on the Bethe lattice to map the ground-state and finite-temperature phase diagrams of the extended Hubbard model, revealing how onsite repulsion suppresses charge ordering to drive transitions between insulating and metallic states while highlighting the method's analytical advantages over purely numerical approaches.
NeuralCrop is a differentiable hybrid model that integrates process-based physics with machine learning to deliver more accurate, computationally efficient, and climate-resilient crop yield projections, particularly under extreme weather conditions, by combining the strengths of traditional global gridded crop models with data-driven optimization.
This paper introduces a Riemannian flow matching framework for Boltzmann generators that incorporates system periodicity and utilizes Hutchinson's trace estimator with cumulant-based bias correction to enable scalable, high-accuracy free energy estimation for large condensed-matter systems like monatomic ice.
The paper introduces SCALE-TRACK, an open-source, asynchronous Euler-Lagrange particle tracking algorithm designed for heterogeneous exascale architectures that achieves unprecedented scalability by successfully simulating up to 256 billion particles across 256 GPUs while maintaining accuracy and efficiency.
This paper presents a MATLAB-based multi-GPU framework that overcomes memory limitations and achieves significant speedups (up to 60x) for large-scale phase-field crystal simulations by implementing two complementary strategies for accelerating two- and three-dimensional fast Fourier transforms.
This paper presents new analytic continuation results for the dynamic structure factor of the uniform electron liquid across a broad temperature range by comparing traditional maximum entropy and sparse Gaussian kernel methods applied to *ab initio* path integral Monte Carlo data, with implications for x-ray Thomson scattering experiments and time-dependent density functional theory.
This paper introduces a novel method using cross-scale graph-pooled Chebyshev signatures to automatically characterize, classify, and cluster complex atomistic structural transitions in large-scale simulations by defining a physically meaningful distance metric that outperforms traditional analysis techniques.
This paper presents a neural-operator-accelerated concurrent multiscale framework that couples atomistic simulations with continuum finite-element analysis using a Recurrent Neural Operator surrogate to efficiently and accurately model the history-dependent viscoelastic behavior of materials like polyurea at scales previously untractable for direct molecular dynamics coupling.