903 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 introduces three complementary Python packages—Tensor-Network-Visualization, Tensor-Network-Editor, and Quantum Circuit Drawer—that provide a visual authoring and inspection layer for tensor networks and quantum circuits to facilitate structural debugging, code generation, and design-level analysis without implementing new simulation algorithms.
This paper combines analytical theory and numerical simulations to demonstrate that fluid injection rate governs fault-gouge failure by creating pressure heterogeneity, where slow injection causes uniform weakening while rapid injection preserves strength in distal regions, thereby offering a refined framework for predicting seismicity in geotechnical operations.
This paper presents a new framework using the Athena++ code with multigroup radiation transport and radial rays to accurately and efficiently model frequency-dependent absorption and scattering in stellar-irradiated protoplanetary disks, achieving temperature results within 2–5% of Monte Carlo benchmarks while significantly reducing computational costs.
This paper introduces DMCD, a memory-efficient Diffusion Monte Carlo algorithm that replaces the traditional breadth-first swarm approach with a depth-first, stack-based traversal to unify particle transport and eigenvalue problem treatments while effectively managing walker pools.
By combining machine-learning interatomic potentials with deep-learning Hamiltonians, this study reveals that a continuous three-dimensional Bi-S network is the central motif responsible for stabilizing cation-disordered AgBiS2 and maintaining its dispersive conduction-band edge and small electron effective mass despite strong structural disorder.
This paper introduces a mathematical framework based on Lippmann-Schwinger integral equations to model acoustic wave scattering in time-dependent media with velocity-modulated interfaces, demonstrating space-time duality and experimentally validating the theory to enable wave scattering analysis without prior knowledge of background fields.
This paper presents a generalized, data-driven kinetic model for one-component plasmas that extends beyond the weakly coupled regime by incorporating anisotropic, non-stationary collision kernels learned from molecular dynamics, and introduces a fast spectral separation method to enable efficient, structure-preserving numerical simulations with complexity.
This paper presents a machine learning-driven, physics-guided reduced model for predicting Electron Temperature Gradient (ETG) turbulence heat flux in the Wendelstein 7-X stellarator, which achieves high accuracy through active learning and radial interpolation but reveals that a single radius-independent formulation is insufficient to capture the device's geometry-dependent transport physics.
This work demonstrates that exterior complex scaling transforms non-decaying scattering wave functions into exponentially decaying forms, enabling physics-informed neural networks to accurately solve nuclear scattering problems for the first time and paving the way for efficient inverse problems and complex reaction modeling.
This paper introduces a generative flow matching method that accurately captures non-Markovian and non-Gaussian effects in short-time stochastic particle dynamics, outperforming traditional regularized Dean-Kawasaki models in predicting statistical moments and first passage times.