922 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 evaluates the EUCLID framework for the automated discovery of hyperelastic constitutive laws using experimental data from natural rubber specimens, comparing its performance against conventional parameter identification methods in terms of predictive accuracy, generalization to unseen geometries, and coverage of the material state space.
`diffpy.morph` is an open-source Python package designed to reveal meaningful scientific insights from 1D spectra by applying "morphs" to datasets to remove uninteresting differences, such as experimental inconsistencies or thermal expansion, during model-independent comparisons.
This paper proposes a physics-informed multimodal conditional modeling framework using Mixture Density Networks (MDNs) that embeds physical laws through component-specific regularization to accurately and interpretably capture non-unique scientific phenomena.
This paper investigates how inconsistencies between experimental/numerical data and governing equations create a "consistency barrier" that sets an intrinsic lower bound on the accuracy of Physics-Informed Neural Networks (PINNs).
This study utilizes response theory to evaluate the effectiveness of linear and quadratic approaches in modeling electron charge dynamics and separation, demonstrating that quadratic response theory provides a more accurate description of charge transfer processes than linear response.
This first-principles DFT study investigates the thermodynamic, kinetic, optical, and mechanical properties of () hydrides, identifying as the most promising candidate for hydrogen storage due to its superior storage capacity.
This paper develops an efficient, high-order compact gas-kinetic scheme in an arbitrary Lagrangian-Eulerian (ALE) formulation that achieves high spatial and temporal accuracy through a single-stage time-accurate flux evolution and a simplified fourth-order reconstruction to minimize the computational costs associated with mesh motion.
This paper presents a machine learning approach using Gaussian process regression and long-range descriptors to predict electron densities in large-scale moiré superlattices, enabling the efficient modeling of complex quantum phenomena that were previously computationally prohibitive for traditional ab initio methods.
This paper introduces a novel, fully differentiable simulation framework that unifies computational fluid dynamics and machine learning, enabling end-to-end optimization and data-driven modeling for multi-scale flows across both continuum and rarefied regimes.
This paper demonstrates that applying an optimal parameterization strategy—which uses estimated local mean first passage times to guide trajectory pruning and replication—significantly reduces the variance and improves the reliability of weighted ensemble simulations for complex, high-dimensional molecular folding processes.