1259 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 novel reconstruction framework featuring distance-based masking and adaptive alpha-shape methods to accurately recover physical boundaries from scattered 2D CFD data for CNN-ready grids, offering significant speedups, minimal tuning requirements, and a companion web application for end-to-end processing.
The paper introduces Uni-Flow, a unified autoregressive-diffusion framework that decouples temporal evolution from spatial refinement to enable faster-than-real-time, high-resolution modeling of complex multiscale flows across turbulent and physiological regimes.
This paper presents a physics-informed, data-driven, and parameter-free symbolic subgrid-scale model for incompressible fluid turbulence that utilizes a rank-two tensor field to accurately predict energy and enstrophy fluxes across diverse coherent structures, outperforming leading LES models without relying on restrictive phenomenological assumptions.
This paper presents a novel Bayesian inference method using a machine-learning surrogate model trained on GPU-accelerated simulations to accurately determine the spatially varying impurity density of high-purity germanium detectors from capacitance measurements, overcoming the limitations of traditional manufacturer data.
This paper introduces the Resolution Independent Neural Operator (RINO), a framework that extends DeepONet to handle input and output functions discretized at arbitrary sensor locations by employing dictionary learning with implicit neural representations (specifically SIRENs) to project data into a compatible coefficient space.
This paper proposes an efficient ILUES-based adaptive Gaussian process method that iteratively constructs a surrogate for the unnormalized posterior density and employs a Gaussian mixture MCMC sampler to accurately solve multimodal Bayesian inverse problems with limited forward model evaluations.
This paper introduces a novel algorithm that iteratively combines auxiliary-field quantum Monte Carlo with tensor-train sketching to generate high-fidelity trial wavefunctions, thereby significantly improving sampling efficiency and reducing variance in calculating the ground-state energy of large quantum many-body systems.
This paper presents a robust and scalable direct-forcing immersed boundary solver based on a preconditioned SIMPLE algorithm that utilizes a discrete Laplacian preconditioner to achieve grid-independent convergence for accurate two-way fluid-structure interaction simulations with significant added-mass effects.
This study proposes an unsupervised, physics-informed neural network approach that integrates train load data and bridge response with governing differential equations to effectively identify, localize, and quantify damage in steel truss railroad bridges while incorporating prior inspection knowledge.
This paper introduces characteristic boundary conditions, including Navier-Stokes characteristic boundary conditions and a novel generalized characteristic relaxation approach, within the Hybridizable Discontinuous Galerkin framework to effectively minimize wave and vortex reflections at artificial boundaries in both inviscid and viscous weakly compressible flows.