1164 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.
Through numerical hydrodynamic simulations, this study reveals that an active mixture of polar and apolar self-propelled components exhibits a unique regime of spatiotemporal chaos characterized by chaotic band-like structures and proliferating topological defects, driven by a non-monotonic response to the polar component's density and activity.
Flow Gym is a JAX-based framework designed to unify the development, benchmarking, training, and deployment of particle image velocimetry (PIV) and optical-flow methods through a standardized interface that enhances reproducibility and bridges the gap between research and experimental applications.
This paper demonstrates that lateral periodic boundary conditions in molecular simulations of interfacial polar media introduce an unscreened zero-mode that causes unphysical, diverging potential fluctuations scaling with system depth or inversely with lateral area, and provides an analytic criterion to select appropriate lateral cell dimensions to avoid this artifact.
This paper proposes a computationally efficient, plug-and-play framework for probabilistic constitutive modeling of anisotropic soft tissues that leverages conformalized quantile regression on a thermodynamically consistent, polyconvex formulation to explicitly quantify predictive uncertainty without requiring distributional assumptions.
This paper introduces a model charge density approach to Ewald summations that accelerates convergence by canceling multipole moments of the crystalline charge distribution, a method validated on gallium arsenide and shown to clarify long-standing implementations in the CRYSTAL code.
This paper demonstrates that incorporating quantum-chemical bonding descriptors into machine learning models significantly improves the prediction of elastic, vibrational, and thermodynamic properties of approximately 13,000 solid-state materials while also enabling the discovery of intuitive physical expressions for these properties.
This paper introduces a physics-aware machine learning surrogate for the 3D stochastic Cahn-Hilliard equation that parameterizes inter-cell fluxes to guarantee mass conservation and thermodynamic interpretability, enabling the accurate simulation of noise-driven phenomena like nucleation and coarsening with significant generalization to larger spatial and temporal scales.
This paper introduces the variational free energy surface (VaFES), a dataset-free framework that leverages reversible generative models to directly construct differentiable free energy surfaces and instantly generate rare-event configurations without requiring prior simulation data.
This paper presents a GPU-accelerated ultrasonic goniometry method that utilizes a plane-wave model, optimal zeroth-order bounds, and a wave-fitting approach to efficiently characterize the general anisotropic elasticity of materials without requiring precise sample alignment.
This first-principles study investigates how sodium intercalation into layered potassium birnessite alters its structural stability, ion diffusion barriers, vibrational modes, and electronic properties, revealing that the process induces significant lattice distortions and transforms the material into a tunable bipolar magnetic semiconductor with potential applications in energy storage and spintronics.