917 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 establishes a first-principles theory of vacuum Wannier functions that unifies tight-binding and nearly-free-electron models to enable predictive photoemission calculations without semiempirical potentials by constructing dense k-space scattering states at solid-vacuum interfaces.
This paper introduces Excited Pfaffians, a generalized neural network architecture combined with Multi-State Importance Sampling, which enables the efficient and accurate representation of multiple excited states and potential energy surfaces with nearly constant computational cost, achieving significant speedups and scalability for systems like the carbon dimer and beryllium atom.
This paper introduces a robust, real-time machine learning framework using a one-dimensional convolutional neural network to efficiently and accurately analyze Nitrogen-Vacancy center ODMR spectra, outperforming conventional nonlinear fitting in speed and reliability—particularly at low signal-to-noise ratios—as demonstrated in intracellular temperature sensing and superconducting vortex imaging.
This paper extends the neural network backflow approach to ab-initio solid-state calculations by introducing a scalable two-stage pruning strategy that efficiently manages massive configuration spaces, achieving state-of-the-art accuracy for diverse materials like hydrogen chains, graphene, and silicon while outperforming traditional methods in strongly correlated regimes.
This paper presents a fully reproducible demonstration of an AI-assisted scientific workflow on canonical physics and mathematics benchmarks, illustrating how contemporary AI can effectively serve as a trustworthy copilot for derivation, implementation, and validation when guided by human oversight and rigorous verification.
This paper demonstrates that a hypothetical network of 175 seafloor pressure sensors, combined with a Bayesian inversion framework utilizing offline precomputation and online data assimilation, can enable real-time probabilistic tsunami forecasting for Cascadia earthquakes with high accuracy and sub-second latency despite sparse offshore observations.
This paper proposes a novel convolutional autoencoder neural ODE (CAE-NODE) framework that successfully constructs a highly compressed, physically consistent latent manifold to accurately predict the full transient dynamics of 2D counterflow flames, including ignition and propagation, with relative errors below 2%.
This paper introduces ANNA, a MATLAB and Python toolbox that efficiently computes Newtonian noise for the Einstein Telescope by integrating seismic wave fields defined on 3D finite element meshes, with its accuracy verified against analytical solutions for both full-space and half-space scenarios.
This paper proposes a lightweight, post-hoc method that generates interpretable, pointwise error estimates for Physics-informed Neural Networks (PINNs) by solving an error equation via finite difference methods using the PINN's PDE residual, thereby enhancing trust in their predictions without requiring knowledge of the true solution.
This paper introduces a novel hybrid Waveguide Neural Operator (WGNO) and evaluates Physics-Informed Neural Networks (PINNs) for simulating Extreme Ultraviolet (EUV) wave diffraction from lithography masks, demonstrating that these neural systems achieve state-of-the-art accuracy with significantly faster inference times and strong generalization compared to traditional numerical solvers.