1255 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 proposes an enhanced data-driven reduced-order model combining autoencoders and neural ordinary differential equations with a novel latent gradient loss term, demonstrating significantly improved accuracy, robustness, and computational efficiency for simulating stiff hydrogen and ammonia-air ignition dynamics compared to traditional methods.
FFTArray is a modular, open-source Python library built on the Array API Standard that simplifies the implementation of discretized multi-dimensional Fourier transforms for pseudo-spectral methods by automating complex grid and scaling corrections while ensuring compatibility with diverse backends like NumPy, JAX, and PyTorch.
This paper develops a hierarchical system of ordinary differential equations to model higher-order spatial correlations in SIS processes on random regular graphs, significantly improving the accuracy of infection density predictions compared to traditional mean-field and pairwise approximations.
This paper introduces the potential-barrier affinity (PBA) effect, a quantum phenomenon where electrons accumulate in interatomic regions when their energy exceeds the potential barrier, thereby overturning traditional views on electride localization and establishing a new fundamental mechanism for chemical bonding and material design.
This paper proposes an adaptive patching scheme that exploits block-sparse Quantics Tensor Train (QTT) structures to efficiently reduce computational costs for large bond dimensions, thereby enabling practical large-scale calculations of complex problems like bubble diagrams and Bethe-Salpeter equations.
This paper proposes a physics-informed video diffusion framework that integrates physical constraints directly into the generative process to simultaneously produce realistic, temporally coherent water flow videos and accurate physical states based on shallow water equations, outperforming both purely data-driven models and traditional simulation pipelines in terms of fidelity and speed.
This paper introduces a unified variational framework that integrates phase-field fracture and third-medium contact within finite deformation hyperelasticity by regularizing both crack topology and contact interfaces, thereby eliminating the need for explicit tracking algorithms while successfully simulating complex coupled phenomena like secondary crushing in Brazilian disk tests.
This paper presents an optimized protocol using artificial neural networks to identify solvents in starch-liquid slurries from post-desiccation fracture patterns with 96% accuracy by analyzing nine morphological features, particularly crack area distribution.
This paper introduces an unsupervised physics-informed neural network with a specialized window parametrization strategy to robustly solve saddle-point equations for strong-field ionization across tailored laser fields, offering a stable alternative to conventional solvers for computing photoelectron momentum distributions and enabling systematic exploration of complex semiclassical regimes.
This paper presents a physics-constrained neural network closure for Lattice Boltzmann Large-Eddy Simulation that, by integrating data-driven learning with physical constraints like rotational equivariance and energy transfer matching, achieves superior statistical accuracy and production-ready performance compared to traditional Smagorinsky baselines.