1258 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 presents a robust and unconditionally stable space-time Galerkin time-domain boundary element method for predicting acoustic scattering and shielding by complex geometries, which is validated through analytical cases and successfully applied to a practical trailing edge-mounted propeller scenario with good agreement to experimental data.
This paper proposes a scalable physics-informed deep generative model (sPI-GeM) that overcomes the limitations of existing methods by effectively solving forward and inverse stochastic differential equations in high-dimensional stochastic and spatial spaces through a combination of physics-informed basis networks and a deep generative model.
This paper proposes an accurate and memory-efficient sum-of-Gaussians tensor neural network (SOG-TNN) algorithm that overcomes the curse of dimensionality and handles Coulomb singularities in high-dimensional Schrödinger equations through a low-rank tensor representation and a novel range-splitting scheme for electron-electron interactions.
This paper presents a Hybrid High-Order (HHO) formulation for variable density incompressible flows that ensures exact volume conservation and pure density advection through a combination of hybrid spatial discretization and ESDIRK time stepping, demonstrating robustness, pressure-independence, and high-order accuracy in simulating immiscible fluid mixtures and Rayleigh-Taylor instabilities.
Using extensive parallel-tempering Monte Carlo simulations, this study reveals that in the three-dimensional random-bond Ising model, a secondary percolation transition involving two equal-density clusters occurs above the thermodynamic ordering points, with the subsequent divergence of these cluster densities serving as a distinct percolation signature for the ferromagnetic and spin-glass phase transitions.
This study utilizes atomistic and multiscale simulations to demonstrate that misfit and threading dislocations significantly reduce the surface energies of PbTe-PbSe interfaces, with heteroepitaxial processes yielding up to 50% lower energy compared to coherent interfaces.
This paper introduces the Open Polymers 2026 (OPoly26) dataset, a publicly released collection of over 6.57 million density functional theory calculations on polymeric systems designed to overcome previous computational limitations and enhance machine learning models for predicting polymer properties.
This paper proposes a novel symmetry-driven generative framework that combines large language models for chemical semantics with a linear-complexity heuristic beam search to rigorously enforce algebraic consistency in Wyckoff patterns, thereby overcoming the combinatorial bottleneck in crystal structure prediction to achieve state-of-the-art performance in discovering new materials without relying on existing databases.
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 study demonstrates that incorporating biologically motivated, state-dependent synaptic feedback into a Lipkin-Meshkov-Glick quantum brain model significantly reshapes its phase diagram by expanding the paramagnetic phase and displacing critical boundaries, a phenomenon rigorously characterized through ground-state Husimi distributions, Wehrl entropy, and mean-field dynamical analysis.