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 demonstrates that optimized single-precision simulations on consumer-grade GPUs can achieve accuracy comparable to double-precision methods while delivering a 27-fold speedup, enabling efficient large-scale modeling of complex nematohydrodynamic systems through the implementation of a shifted distribution function and an optimal finite-difference time step.
This paper introduces a Model Context Protocol (MCP) framework that empowers researchers without programming expertise to perform meta-optics inverse design by enabling large language models to autonomously access verified code templates and documentation, achieving high-quality results through structured prompting while eliminating the need for specialized solver knowledge.
This study utilizes fluctuational electrodynamics and modal analysis to demonstrate that near-field radiative heat transfer between polaritonic SiC, SiN, and SiO2 subwavelength membranes can be significantly enhanced or attenuated by up to 5.1-fold and 2.1-fold, respectively, due to material-loss-dependent corner and edge modes that alter the electromagnetic state density.
This study uses lattice Boltzmann simulations to demonstrate that the spontaneous rotation and directed propulsion of elastic capsules in 2D active nematic fluids are governed by the interplay between capsule geometry, flexibility, and the dynamics of confined active topological defects.
This paper introduces dilated recurrent neural network wave functions as a computationally efficient alternative to transformers that successfully captures long-range correlations in quantum states by injecting an explicit geometric inductive bias, thereby overcoming the exponential decay limitations of standard RNNs in critical and highly entangled systems.
This paper introduces a robust charge pumping simulation method that enables the accurate calculation of topological invariants, such as Chern numbers, for many-body systems using neural network wavefunctions, thereby overcoming a key bottleneck in studying correlated topological matter and facilitating the identification of exotic states like anomalous composite Fermi liquids.
This paper presents an active learning workflow that integrates density functional theory, thermochemical modeling, and machine learning to screen over 70 billion candidates, resulting in a generalizable predictive model and the largest public database of CHNO explosives to date, which reveals oxygen balance as the primary driver of detonation performance.
This paper presents a formal proof demonstrating that average magnetization is the primary driver of shape effects in large magnetic samples, leading to a computationally efficient method for incorporating sample shape into micromagnetic simulations using 3D periodic boundary conditions.
This paper introduces a novel resolution-parameterized objective function called generalized bipartite modularity density () that effectively detects hierarchical community structures in both weighted and unweighted bipartite networks without altering their intrinsic topology, outperforming conventional methods in recovering mesoscale and fine-scale organization.
EquiformerV3 advances the third generation of SE(3)-equivariant graph attention Transformers by introducing optimized software, architectural refinements like equivariant merged layer normalization and smooth-cutoff attention, and novel SwiGLU- activations to significantly enhance efficiency, expressivity, and physical consistency, thereby achieving state-of-the-art performance on major 3D atomistic modeling benchmarks.