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 theoretically and numerically demonstrates that in successive inverse Compton scattering, the longitudinal momentum spread of an electron beam converges exponentially to an equilibrium state through the balance of quantum excitation and radiation friction, highlighting the necessity of accounting for cumulative transverse dynamics in designing future high-brightness X-ray and gamma-ray sources.
This chapter provides a comprehensive overview of modern domain decomposition methods, tracing their evolution from Schwarz iterations to robust preconditioners for challenging problems while emphasizing theoretical insights, scalable coarse space corrections, and high-performance implementations.
This paper presents a bio-inspired reinforcement learning framework that enables soft synthetic snakes to learn locomotion primitives in simplified terrains and compose them into adaptive strategies for robustly navigating complex, heterogeneous 3D environments reconstructed from real-world data.
Through large-scale time-domain simulations of dense random packings of high-index dielectric particles, this study provides converging dynamical, spectral, and real-space evidence for the three-dimensional Anderson localization of light, demonstrating how late-time fields self-organize into interference-separated, quasi-stationary confined modes.
This paper introduces PDEInvBench, a comprehensive benchmark dataset for PDE inverse problems, and uses it to explore neural network design spaces, revealing that a two-stage training procedure combining parameter supervision with test-time residual fine-tuning, along with PDE derivative inputs and diverse initial conditions, significantly improves parameter estimation performance.
The paper introduces Linac, a high-performance, open-source, parallel implementation of Gaussian elimination over finite fields and floating-point arithmetic using CUDA, designed to accelerate the solution of linear systems for applications such as the analytic reconstruction of scattering amplitudes in quantum field theory.
Coarse-grained molecular dynamics simulations reveal that elastomeric polymer networks rupture at stresses far below covalent bond strength because deformation concentrates on a "minimum shortest path" of bonds, leading to the sequential failure of a small fraction of these critical bonds rather than the simultaneous breaking of the entire network.
This paper introduces WellPINN, a novel workflow that utilizes sequentially trained physics-informed neural networks on shrinking subdomains to accurately model fluid pressure diffusion around wells throughout the entire injection period, overcoming previous limitations in capturing early-stage pressure dynamics.
This paper presents a computationally efficient 3D phonon Boltzmann transport equation solver that overcomes the limitations of the relaxation-time approximation by leveraging the low-dimensional nature of non-equilibrium distributions and the selective alignment of scattering singular modes to accurately model full-scattering-matrix effects in nanoscale devices.
This paper utilizes the electric-field volume-integral-equation method to demonstrate that for vapor cells smaller than half a wavelength, uncertainty in glass relative permittivity is the dominant error source in Rydberg atom electric-field measurements, yielding a total uncertainty of approximately 3.5% that could be reduced to under 1% with more precise permittivity data.