1158 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 presents a stable and efficient Cartesian grid-based boundary integral method that reformulates elliptic and parabolic PDEs into boundary integral equations solved via matrix-free GMRES and finite difference-based integral evaluation, utilizing variables to simplify mesh preservation and enable robust time-stepping for complex moving interface problems like Hele-Shaw flow and Stefan solidification.
This paper introduces active set algorithms for automated, data-driven basis selection within the Atomic Cluster Expansion framework, demonstrating that the resulting sparse linear machine learning interatomic potentials offer superior computational efficiency, generalization accuracy, and interpretability compared to dense models.
This paper introduces a general, adaptive hyperviscosity stabilization procedure for the RBF-FD method that dynamically determines the viscosity constant based on the spectral radius of the evolution matrix to efficiently and stably solve advection-dominated transport equations on unbounded domains without empirical tuning.
This paper demonstrates that laminar-turbulent transition in canonical K-type flow is not a stochastic process but a deterministic sequence of temporal and spatial symmetry-breaking events driven by organized, energetically dominant coherent structures that progressively transform a harmonic response into broadband turbulence.
This paper introduces RedEigCD, a novel self-adaptive timestepping method for reduced-order models of incompressible flows that leverages exact spectral information to achieve stable timesteps up to 40 times larger than full-order models while maintaining accuracy and online efficiency.
This paper presents a computational study demonstrating that a modified interior penalty (MIP) formulation for Diffusion Synthetic Acceleration (DSA) applied to polytopic discontinuous Galerkin discretizations of transport equations maintains robust convergence across various optical and scattering regimes, outperforming the classical symmetric interior penalty (SIP) approach which can lose stability in intermediate conditions.
This paper introduces a nonuniform iterative phasing framework that combines iterative-projection techniques with fast nonuniform Fourier inversion to efficiently reconstruct high-resolution 3D structures from coherent surface scattering imaging data, effectively addressing challenges posed by dynamical scattering, nonuniform sampling, and phase retrieval while validating the approach on simulated nanostructures.
The paper introduces the High-Explosives and Affected Targets (HEAT) dataset, a comprehensive collection of two-dimensional, cylindrically symmetric multi-material shock simulations generated at Los Alamos National Laboratory, designed to fill the critical gap in public data needed for training and validating AI surrogate models of complex explosive-driven physics.
This paper presents an implicit formulation of the Compact-Kernel Material Point Method (CK-MPM) that effectively balances smoothness and locality to reduce numerical noise and artificial contact gaps in large-deformation solid mechanics simulations, offering a viable alternative to both linear and quadratic B-spline MPM approaches.
This paper proposes a differentiable, physics-informed neural operator that efficiently learns the mapping between microstructural configurations and macroscopic failure envelopes for granular materials, enabling rapid forward prediction and inverse identification while ensuring mechanical admissibility through convexity constraints and reducing computational costs via active learning.