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 proposes a multilevel tensor-train (TT) framework for solving nonlinear partial differential equations in a monolithic space-time formulation, utilizing a coarse-to-fine strategy to provide robust initializations for Newton iterations and achieve superior convergence and efficiency across diverse diffusive, convective, and dispersive dynamics.
The paper introduces *dewi-kadita*, an open-source Python library that implements a 3D Couzin-based fish schooling model enhanced by a novel suite of seven entropy-based metrics and an Oceanic Schooling Index (OSI) to provide a more nuanced characterization of collective organization than traditional order parameters.
This paper presents an analytical and numerical study demonstrating that a radially polarized laser pulse propagating through a magnetized plasma generates azimuthally polarized terahertz radiation, with the field amplitude controlled by plasma density and external magnetic field strength.
This paper presents a validated finite element model of intramembranous ossification that simulates the complex cellular and biochemical processes of bone healing in tooth extraction sites to aid in the development of dental surgical strategies.
This paper proposes a method using Tikhonov regularization to improve the stability of reconstructing partial scattering functions from contrast variation small-angle neutron scattering (CV-SANS) data, particularly when dealing with small absolute values and significant differences in singular values.
This paper proposes a robust, fully coupled implicit finite-volume algorithm combined with a front-tracking method to accurately simulate incompressible viscoelastic interfacial flows at high Weissenberg numbers without the need for a log-conformation approach.
This thesis explores the application of machine learning to geophysical fluid dynamics by implementing a data-driven discretization method that predicts stencil coefficients to accelerate and improve the accuracy and stability of shallow water and Euler equation solvers.
The paper introduces ChemNavigator, an agentic AI system that autonomously integrates large language model reasoning with computational chemistry to discover six statistically significant, chemically grounded design rules for organic photocatalysts, outperforming previous machine learning approaches by independently deriving interpretable structure-property relationships through a hypothesis-driven scientific workflow.
This paper proposes a dual variational formulation based on convex duality and Lagrange multipliers to solve partial differential equations lacking a primal variational structure, demonstrating that such problems can be accurately solved via Galerkin discretization using smooth B-splines and RePU neural networks to minimize a convex dual functional.