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 presents a hybrid surrogate-based multilevel Monte Carlo method that significantly reduces computational costs by up to times while maintaining high accuracy in quantifying uncertainties for the Grad-Shafranov free boundary problem in fusion reactor magnetic equilibrium.
This paper introduces a comprehensive benchmark dataset comprising 624 high-resolution 2D samples from numerical simulations, designed to facilitate the development and evaluation of machine learning surrogates for modeling pore-scale CO2-water interactions in carbon capture and storage applications.
This paper presents a unified framework for calculating electrostatic energy and pressure in periodic Coulomb systems by introducing infinite boundary terms and effective pairwise interactions, which allow the treatment of both neutral and non-neutral systems with point charges and continuous charge distributions using a formulation analogous to isolated systems.
This paper addresses the failure of standard 3D point cloud alignment methods in Minkowski space by proposing two novel solutions for finding the optimal Lorentz transformation between inertial frames: a direct least-squares optimization using boost and rotation parameters, and a computationally efficient algebraic approach that generalizes to other matrix Lie groups.
This study presents the first *ab initio* calculation of the nuclear Schiff moment for the F isotope using the no-core shell model, which, when combined with quantum chemistry calculations and experimental data on HfF, establishes the first experimental bound on this moment and its associated pion-nucleon-nucleon coupling constants.
The paper proposes a variational boundary-based tensor network renormalization group algorithm that utilizes globally optimized boundary tensors to construct accurate renormalization projectors, achieving higher precision than existing methods while maintaining the computational complexity of the original TRG approach.
This paper introduces Re4, a novel collaborative agent framework that leverages three specialized LLMs (Consultant, Programmer, and Reviewer) in a "rewriting-resolution-review-revision" loop to significantly improve the accuracy, reliability, and execution success rate of autonomous code generation for complex scientific computing tasks.
This study demonstrates that active matter systems, particularly those forming liquid droplets, serve as robust reservoirs for computing by utilizing nonlinear driving forces to trigger emergent structural and dynamic features that enhance information processing performance regardless of the specific injection method.
This review comprehensively examines how machine learning techniques, particularly in constructing collective variables, optimizing biasing schemes, and leveraging reinforcement and generative learning, are transforming enhanced sampling methods to overcome timescale limitations in molecular dynamics simulations across diverse applications.
This paper introduces NextHAM, a universal deep learning framework combining a novel E(3)-symmetric Transformer architecture and a zeroth-step Hamiltonian correction strategy, alongside a large-scale benchmark dataset (Materials-HAM-SOC), to achieve highly accurate and efficient prediction of electronic-structure Hamiltonians across diverse materials while explicitly accounting for spin-orbit coupling effects.