1190 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 a deep learning framework called PAPS that unifies the solution of transient Fokker-Planck equations for arbitrary parameters and initial distributions via Gaussian mixture representations and a constraint-preserving autoencoder, achieving high accuracy with inference speeds four orders of magnitude faster than Monte Carlo simulations.
The paper introduces gyaradax, a high-performance JAX/CUDA solver for local flux-tube gyrokinetics that achieves significant speedups and native GPU acceleration over legacy Fortran codes like GKW, while demonstrating how agentic workflows guided by human expertise can rapidly translate complex physics simulations to enable advanced machine learning applications in plasma physics.
This paper presents a novel, efficient evolutionary structural optimization method that combines modified bi-directional evolutionary structure optimization (BESO) with the extended finite element method (XFEM) to solve large-scale, high-resolution topology optimization problems by utilizing sub-region material grids for accuracy and coarse elements for computational efficiency.
This paper presents a novel multidimensional upwinding approach for compressible multiphase flows that combines characteristic-space wave decomposition with physical-space reconstruction schemes, including THINC for interfaces and adaptive techniques for shocks, to significantly enhance accuracy, suppress numerical artifacts, and better capture complex physical phenomena compared to traditional methods.
This paper introduces a Lagrangian-based technique that replaces the computationally expensive requirement of DFT calculations with a single coupled-perturbed Kohn-Sham calculation to efficiently generate unbiased atomic forces in ab initio Variational Monte Carlo, thereby improving their consistency with potential energy surfaces and accuracy relative to CCSD(T) benchmarks.
This paper introduces and evaluates a phenomenological "velocity-coupled radial acceleration" model for disk-galaxy rotation curves, demonstrating that while it fits SPARC data comparably to standard NFW and Burkert halo models, its parameters suffer from significant degeneracy that limits their identifiability for the majority of galaxies.
This paper presents a probabilistic modeling approach using parameter distributions to efficiently simulate Cherenkov light yield fluctuations from particle showers in ice and water, offering a more accurate alternative to full Monte Carlo simulations for neutrino telescope experiments.
This paper demonstrates that while rocket-driven Penrose energy extraction in Kerr spacetime is theoretically possible, it is empirically rare (occurring in at most ~1% of broad parameter scans) and requires extreme conditions such as high black-hole spin, highly relativistic exhaust, and finely tuned initial trajectories, with single periapsis impulses proving more propellant-efficient than continuous thrust.
This paper introduces an atom-by-atom inverse design framework that integrates Nano-Topology Optimization with conditional diffusion models to overcome continuum limitations by explicitly accounting for crystal symmetry and surface physics, thereby enabling the discovery of high-performance metallic nanostructures like aluminum nanocantilevers and nanopillars.
This paper presents a "nowcast transform" method to adjust Gutenberg-Richter statistics within regional earthquake ensembles for improved nowcasting and forecasting of large earthquake exceedance probabilities, demonstrating its consistency and application to the Los Angeles region following the 1994 Northridge earthquake.