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.
The paper introduces "El Agente Cuántico," a multi-agent AI system that automates complex quantum simulation workflows by translating natural-language scientific intent into validated computations across diverse software frameworks, thereby lowering technical barriers and enabling more autonomous exploration of quantum systems.
This paper introduces Drifting Models for molecular conformation generation, leveraging a novel Drifting Force Identity to incorporate physical force labels into one-step sampling, thereby achieving million-fold acceleration over traditional molecular dynamics while maintaining perfect structural validity and Boltzmann distribution accuracy.
This study develops and validates a combined analytical and numerical framework linking thermal spray nozzle operating conditions to far-field aeroacoustic signatures and in-flight particle transport, demonstrating that acoustic monitoring can serve as a non-intrusive method for controlling and optimizing the thermal spray process.
This paper demonstrates that the Continuous-Time Koopman Autoencoder (CT-KAE) serves as a lightweight, stable, and efficient surrogate model for long-horizon ocean state forecasting, outperforming autoregressive Transformer baselines by maintaining bounded errors and consistent large-scale statistics over 2083-day rollouts while enabling resolution-invariant predictions.
This paper introduces TRec, a physics-informed muon scattering tomography framework that significantly enhances the detection of missing fuel in sealed microreactor cores by reconstructing curved muon trajectories and incorporating momentum measurements, thereby outperforming conventional methods like PoCA in both sensitivity and speed under realistic cosmic-ray conditions.
This paper demonstrates that transforming molecular Hamiltonians to the adiabatic representation to eliminate spin-orbit coupling inadvertently generates significant non-adiabatic couplings that must be explicitly included to avoid severe errors in rovibronic predictions, providing exact conditions for validity and practical diagnostics for when single-state approximations fail.
Using molecular dynamics based on density functional theory, this study reveals that the complex high-pressure crystal phases of methane arise from the packing of specific supermolecular clusters (icosahedral and polyhedral) where a trade-off between efficient packing and suppressed rotational entropy, driven by orientation-dependent intermolecular interactions, explains the observed non-cubic symmetries and sluggish phase transitions.
This paper provides a comprehensive comparison of explicit relativistic particle pushers used in Particle-in-Cell simulations, demonstrating that a significant class of these schemes can be generalized to arbitrary high-order accuracy and evaluating the performance of their fourth-order variants against second-order counterparts.
This paper introduces B-ODIL, a Bayesian extension of the Optimization of a Discrete Loss (ODIL) method that integrates PDE-based prior knowledge with data likelihood to solve inverse problems with quantified uncertainties, demonstrating its effectiveness through synthetic benchmarks and a clinical application for estimating brain tumor concentration from MRI scans.
This paper presents an energy-conserving contact dynamics framework for arbitrary convex nonspherical rigid-body particles in 2D and 3D that prevents overlap while enabling continuous force evaluation, thereby providing a robust foundation for modeling the structural and transport properties of complex colloidal and granular systems.