917 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 CoK-PCA, a dimensionality reduction method based on the co-kurtosis tensor that outperforms traditional PCA in accurately capturing stiff chemical dynamics and extreme-valued ignition events in combustion datasets by better identifying critical directions in the thermo-chemical state space.
This paper generalizes the widely used Finite Difference Time Domain (FDTD) method to a meshless setting via the Radial Basis Function generated Finite Difference (RBF-FD) approach and evaluates its performance on a simple test problem.
This paper presents a theoretical analysis and numerical validation of the Onsager-Regularized Lattice Boltzmann Method, demonstrating that it achieves higher-order accuracy and mitigates lattice isotropy errors in nonlinear thermohydrodynamic simulations on standard lattices without requiring external correction terms.
This paper demonstrates that in periodically forced mean-field Ginzburg-Landau models, the correct conjugate field at the critical period is the even-Fourier component of the applied field, which governs a universal order parameter scaling of and reveals a distinct parity-dependent scaling rule for mode-resolved deviations.
This paper presents a unified, physics-constrained neural-operator framework that accelerates Direct Simulation Monte Carlo simulations by replacing the Variable Hard Sphere model with a stochastic neural collision kernel for improved generalization and by introducing an efficient surrogate for ab initio Jäger potentials, collectively achieving high-fidelity predictions of rarefied gas dynamics with reduced computational cost.
This study demonstrates that ab initio electronic structure calculations for various molecules exhibit Gaussian orthogonal ensemble statistics characteristic of random matrix theory, confirming its universality in describing complex molecular spectra and predicting specific magnetic field dependencies that induce a transition to Gaussian unitary ensemble behavior at extreme scales.
This study establishes a quantitative framework linking molecular geometry to collective lying-standing transition kinetics at organic-inorganic interfaces, demonstrating that diffusion-assisted mechanisms and footprint ratios drive emergent rate laws that cannot be predicted from single-molecule behaviors.
The paper introduces MBD-ML, a pretrained message passing neural network that directly predicts atomic coefficients and polarizabilities from structures to enable efficient, accurate, and seamless integration of many-body dispersion interactions into various electronic structure codes and force fields without intermediate electronic calculations.
This paper introduces a generalized characteristic mapping method that utilizes a semi-Lagrangian approach with Duhamel integrals to efficiently solve non-linear advection with source terms, demonstrating third-order accuracy and exceptional resolution in ideal magnetohydrodynamics simulations.
This review presents the first unified conceptual framework for scientific machine learning in hydrology, organizing fragmented methodological families into a coherent structure to clarify novelty, foster cumulative progress, and guide future research directions.