1244 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 Generative Replica Exchange (GREX), a novel framework that integrates deep generative models and normalizing flows into replica exchange simulations to eliminate the need for multiple intermediate temperature replicas, thereby significantly accelerating molecular sampling while maintaining thermodynamic rigor.
This paper introduces "blurred sampling," a rigorous and efficient post-processing framework that eliminates statistical pathologies caused by nodal and support-mismatch issues in Variational Monte Carlo, thereby enabling robust and unbiased optimization and dynamics simulations for both continuous and discrete quantum systems.
This paper presents a hybrid quantum-classical solver that integrates the Harrow-Hassidim-Lloyd (HHL) algorithm with Chebyshev-based approximate quantum state tomography to efficiently solve the incompressible Navier-Stokes equations, successfully validating the approach through accurate simulations of lid-driven cavity and Taylor-Green vortex flows using IBM's Qiskit framework.
This paper proposes and demonstrates a multi-outcome optimization strategy for photonic quantum circuits that significantly enhances the success probability of generating non-Gaussian states by leveraging multiple useful measurement patterns and aggregating degenerate outcomes, rather than restricting optimization to a single target outcome.
This paper proposes a high-order wavelet-based grid adaptation strategy that utilizes 1D polynomial extrapolation to consistently handle sharp immersed boundaries, thereby enabling robust and predictable error control for solving PDEs on complex, moving geometries.
This study utilizes Direct Numerical Simulations to demonstrate that thermodiffusive instabilities in turbulent lean premixed hydrogen-air flames significantly enhance low-frequency combustion noise by altering heat release fluctuations and flame surface dynamics through the coupled action of turbulence and flame stretch, distinguishing their acoustic behavior from stable methane flames.
This paper argues that for quantum computational chemistry to achieve utility-scale impact, algorithm development must evolve beyond solving isolated, strongly correlated problems to enable the practical integration of quantum-accelerated methods into high-throughput pipelines for routine calculations on arbitrary molecules.
This paper presents a stable and efficient numerical method for solving acoustic multibody scattering problems in two and three dimensions by combining local Method of Fundamental Solutions (MFS) approximations with a global iterative solver, achieving high accuracy and scalability without the implementation complexity of traditional boundary integral discretization techniques.
This study predicts that phosphorus carbide nanotubes (NTs) are a stable, chemically realistic quasi-one-dimensional material that uniquely hosts coexisting Dirac fermions and robust flat bands at the Fermi level, while exhibiting strain-induced structural and quantum phase transitions, localized edge states, and tunable magnetism for potential applications in quantum hardware and spintronics.
This paper introduces Effective Field Neural Networks (EFNNs), a novel architecture leveraging continued functions from renormalization theory to accurately model classical and quantum many-body systems with superior generalization to larger lattice sizes and significant computational speedups compared to exact diagonalization and standard deep learning models.