1259 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 introduces ArGEnT, a Transformer-based architecture that encodes arbitrary geometries directly from point clouds to enhance DeepONet's ability to learn solution operators for complex physical systems without explicit geometric parametrization, thereby achieving superior generalization and accuracy across fluid dynamics, solid mechanics, and electrochemical applications.
This paper presents a GPU-accelerated, list-indexed explicit time-stepping (LIETS) algorithm that drastically accelerates the generation of high-resolution 3D porous media by restricting stochastic growth operations to an active front, reducing computation time for a 400³ domain to approximately 24 seconds while accurately reproducing experimental permeability-porosity trends.
This paper introduces an unsupervised Fourier Vision Transformer (Fourier ViT) that effectively solves the challenging multi-domain phase retrieval problem in Bragg coherent diffraction imaging by globally coupling reciprocal-space information, thereby outperforming classical iterative solvers and convolutional neural networks in robustness and accuracy for reconstructing crystals with strong-phase distortions.
This paper develops an analytical framework to evaluate stochastic Verlet-type integrators by deriving closed-form expressions for their accuracy in simulating diffusion, drift, and harmonic potential sampling, ultimately identifying the GJ set of integrators as the most robust option for high-quality thermodynamic simulations.
This paper proposes HMT-PF, a hybrid Mamba-Transformer model that utilizes a physics-informed, self-supervised fine-tuning mechanism and a point query mechanism to generate accurate spatiotemporal physical fields on unstructured grids while minimizing physical equation discrepancies.
This paper introduces a model of Dirac-Bianconi driven oscillators that couple topological signals on nodes and links within higher-order networks, utilizing phase reduction to analyze their synchronization dynamics beyond traditional node-based paradigms.
This paper introduces evortran, a modern, high-performance Fortran library for genetic algorithms that offers flexible, efficient evolutionary optimization tools and demonstrates its effectiveness through applications ranging from LHC data fitting to LISA gravitational wave signal reconstruction.
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.