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 **peapods**, a high-performance, open-source Python package leveraging a Rust core to efficiently simulate Ising spin systems on periodic Bravais lattices using a comprehensive suite of single-spin-flip, cluster, and replica-based Monte Carlo algorithms validated against exact critical temperatures.
This paper proposes a general framework for adaptive sensing of physical dynamical systems, where a trainable attention module learns optimal spatial probing and measurement combination to significantly improve prediction accuracy, effectively reframing neural networks as trainable devices for extracting information from physical systems.
This paper identifies that measurement-feedback Ising machines suffer from a significantly reduced range of effective hyperparameters compared to their time-continuous analog counterparts due to discrete operation, and proposes an experimentally verified method to mitigate this sensitivity.
This paper introduces a nonparametric framework that optimizes reaction coordinates by incorporating trajectory histories to overcome standard machine learning limitations, enabling robust characterization of rare event dynamics in complex systems like protein folding and climate models without requiring extensive sampling or ground truth data.
This study reveals that Vicsek agents with mutually repelling interactions in a complex noise environment featuring a noiseless circular core exhibit emergent rotational order and a re-entrant global flocking state at high outer noise levels, while demonstrating that particle velocity governs escape dynamics and that gradual noise gradients significantly suppress collective order compared to sharp environmental transitions.
This paper develops a moment-based quasilinear theory to demonstrate that cold electron populations drive secondary instabilities which can nearly completely damp parallel-propagating whistler chorus waves, thereby limiting their amplitude and explaining the rare simultaneous observation of high-amplitude field-aligned and oblique whistler waves in Earth's magnetosphere.
This paper introduces a unified, software-assisted methodology to achieve machine-precision cross-verification and fair performance benchmarking across three major open-source DDA solvers (DDSCAT, ADDA, and IFDDA) by aligning numerical parameters and providing equivalence tables for reproducible, interoperable electromagnetic scattering simulations.
This paper investigates geometric regularization strategies for autoencoder-based reduced-order models with neural ODE dynamics, finding that while near-isometry, stochastic gain, and curvature penalties often hinder long-horizon latent dynamics training despite improving local smoothness, Stiefel projection of the first decoder layer consistently enhances conditioning and rollout performance by better addressing latent-geometry mismatch.
This paper presents a method to derive the exact elastic continuum model for any discrete spring network based solely on its geometry and topology, enabling the prediction of mechanical properties—including non-affine displacements and auxetic behavior—without the need for computationally expensive simulations.
This paper proposes "Astral," a novel training loss function for physics-informed neural networks based on error majorants that provides reliable, tight upper bounds on solution errors and superior spatial correlation compared to traditional residual minimization, enabling accurate error estimation and more efficient convergence across diverse partial differential equation problems.