1258 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 systematically reviews and analyzes the correspondence between discrete linear lattice models (ranging from infinite to finite with various boundary conditions) and their continuous partial differential equation counterparts, utilizing Fourier analysis to examine their relationship primarily through the lens of dispersion relations.
This paper identifies a fundamental "parameter gap" in analog Ising machines that prevents guaranteed convergence to solutions and proposes a hybrid dynamical framework to reshape bifurcation topology, thereby offering a unified principle for optimizing these systems.
The paper introduces Matlantis-PFP v8, a universal machine learning interatomic potential trained on the more accurate r2SCAN functional rather than PBE, which achieves systematically improved agreement with experimental data and high-accuracy references across diverse chemical domains without requiring domain-specific fine-tuning.
This paper demonstrates that differentiable programming, enabled by automatic differentiation, serves as a versatile framework in plasma physics that not only accelerates traditional design and inference tasks but also enables novel capabilities such as discovering new nonlinear phenomena, learning hidden kinetic variables for fluid models, and performing high-dimensional inverse design.
This study extends the STACIE algorithm by incorporating a Lorentz model and additional pressure tensor components to enable reliable, automated, and uncertainty-quantified viscosity calculations for 2,2,4-trimethylhexane under high pressures, demonstrating that long equilibrium molecular dynamics simulations can achieve experimental accuracy (<6% error) where previous methods failed due to insufficient sampling.
This paper introduces a fermi-bose bootstrap embedding framework that combines correlated electron treatments with coherent-state mean-field phonons to efficiently simulate large interacting electron-phonon systems, demonstrating high accuracy and runtime advantages in localized regimes like Mott insulators while acknowledging limitations in weakly coupled, delocalized regions due to mean-field approximations.
This paper presents a rapid and robust simulation workflow that combines the toroidal STRIDE code with the two-fluid slab layer solver SLAYER to accurately predict classical tearing mode stability and growth rates across a wide range of reactor-relevant plasma conditions.
This paper introduces a stochastic single-stage stellarator optimization method that combines fixed-boundary MHD equilibria with randomly perturbed coils to avoid sharp local minima and produce more robust quasi-symmetric configurations with improved flux, symmetry, and particle confinement compared to existing deterministic and two-stage approaches.
This paper proposes a self-consistent, numerically efficient simulation model based on a scattered-field formulation that couples geometric wakefield and space charge effects to accurately assess their non-negligible impact on beam quality in high-brilliance electron sources like the SuperKEK photo-gun.
This paper introduces Proof-Carrying Materials (PCM), a rigorous framework combining adversarial falsification, statistical refinement, and formal Lean 4 certification to overcome the high failure rates of single machine-learned interatomic potentials, thereby significantly improving the reliability and discovery yield of high-throughput materials screening.