1201 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.
The paper introduces pylevin, a Python package that utilizes Levin's method to efficiently and accurately compute highly oscillatory integrals containing up to three Bessel functions, offering performance comparable to specialized single-function tools and significantly outperforming standard quadrature methods for multi-function integrals.
This paper introduces a new fixed-point iteration scheme based on the Bidirectional Pulse Propagation Equations to solve nonlinear electromagnetic scattering problems in slab-like geometries with arbitrary material responses, demonstrating its convergence and accuracy through a case study involving mixed fast electronic and slow molecular vibrational responses.
This paper introduces a coupled Lindblad pseudomode theory that achieves efficient non-Markovian quantum dynamics simulation with polylogarithmic scaling in time and precision, featuring a robust numerical algorithm that eliminates non-convex optimization and demonstrates effectiveness across classical and quantum platforms.
This paper proposes a quantum-inspired BQP optimization algorithm to efficiently identify magnetic spin distributions in ferromagnetic materials by minimizing free energy, successfully addressing the computational intractability of large-scale Ising model problems where conventional methods like genetic algorithms fail.
This paper introduces "split-flows," a novel flow-based framework that treats backmapping as a continuous-time measure transport to enable expressive conditional sampling of atomistic structures and, for the first time, provide a tractable method for computing mapping entropies to quantify information loss in coarse-grained molecular models.
This paper presents a first-principles framework based on density functional perturbation theory that adapts phonon calculations for nanostructures with cyclic and helical symmetry, demonstrating high accuracy and physical consistency through applications to carbon nanotubes.
This paper introduces rigorous metrics to analyze how unconstrained machine learning models learn physical symmetries, demonstrating that strategically injecting minimal inductive biases can achieve superior stability and accuracy while preserving the scalability of unconstrained architectures.
This study demonstrates that while quantum effects significantly influence hydrogen permeation through graphdiyne membranes, classical molecular dynamics simulations combined with Feynman-Hibbs corrections can reliably bound these results, provided that the crucial thermal motion of the membrane is included to accurately capture the reduction in permeation barriers.
This paper presents a GPU-accelerated multigrid Gaussian-Plane-Wave density fitting algorithm implemented in PySCF's GPU4PySCF module, which achieves up to 25x speedup over CPU implementations for large-scale Kohn-Sham DFT calculations while maintaining high efficiency for high angular momentum functions.
This study applies Proper Orthogonal Decomposition (POD) and four Dynamic Mode Decomposition (DMD) variants to analyze unsteady flow in a 1.5-stage axial turbine, revealing that while specific DMD methods achieve reconstruction accuracy comparable to POD and better capture the system's true dynamic frequencies, both approaches identify dominant modes whose characteristics correlate with the turbine's adiabatic efficiency across different stator clocking configurations.