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 presents a novel single-grid, Eulerian-VOF model within the open-source Basilisk framework that fully resolves coupled solid-gas dynamics and anisotropic particle deformation during biomass pyrolysis, demonstrating excellent agreement with experimental data while providing a robust tool for developing sustainable pyrolysis processes.
This study combines Density Functional Theory and Reactive Molecular Dynamics simulations to demonstrate that B-N co-doping, particularly in ortho configurations, optimizes hydrogen adsorption thermodynamics and enhances thermal stability for the hydrogen evolution reaction in - and 6,6,12-graphyne, whereas other doping patterns or pristine lattices suffer from weak activity or structural degradation.
This paper proposes and validates an "interaction annealing" approach that suppresses charge fluctuations to reveal the effective quantized valence and orbital structure of correlated materials, successfully explaining complex phenomena like ferro-orbital order in WTe and Mott insulation in LaCuO.
This paper introduces Multireference-state Error Mitigation (MREM), an advanced quantum error mitigation technique that utilizes compact multireference states constructed via Givens rotations to significantly improve the accuracy of quantum chemistry calculations for strongly correlated molecular systems, overcoming the limitations of traditional Reference-state Error Mitigation.
This paper presents a generalized, platform-agnostic full-spectrum modeling framework for mobile gamma-ray spectrometry systems in scattering media that achieves near-real-time template generation with a computational speedup of and high accuracy, significantly enhancing capabilities for source localization and quantification across diverse environmental and emergency response applications.
This paper proposes Residual-Gated Adaptive KANs (RGA KANs), a novel architecture combining a basis-agnostic initialization scheme with residual gating, to overcome the training instability and divergence issues of deep physics-informed Kolmogorov-Arnold Networks, thereby achieving superior accuracy and stability across diverse partial differential equation benchmarks.
This paper presents a differentiable quantum-trajectory simulation method using score-function gradient estimators to efficiently infer quark-gluon plasma transport coefficients from quarkonium suppression data by enabling gradient-based optimization on open-source Monte Carlo simulations of Lindblad dynamics.
This paper proposes a variance reduction technique for the Fokker-Planck particle method in rarefied gas simulations by integrating Array-Randomized Quasi-Monte Carlo (Array-RQMC) with quasi-random numbers, demonstrating improved convergence rates and reduced estimator errors compared to traditional pseudo-random sampling and other variance-reduction methods.
LiDRoSIS is an automated MATLAB-Python platform that integrates image processing and statistical analysis to enable reproducible, high-throughput quantification of lipid droplets and reactive oxygen species in irradiated cells treated with gold-based nanoparticles.
This paper reports the experimental measurement and relativistic coupled-cluster theoretical prediction of the ionization potential of radium monofluoride (RaF) as 4.969 eV, alongside an improved calculation of its dissociation energy, confirming that RaF is a unique diatomic molecule where the dissociation energy exceeds the ionization potential.