1164 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 a Padé approximation method to efficiently compute Fisher zeros and analyze phase transitions in two-dimensional Ising and XY models, significantly reducing computational costs and polynomial degrees while maintaining accuracy and overcoming convergence issues found in previous approaches.
This paper presents a methodology that leverages differentiable kinetic simulators and plasma phase space data to learn time-dependent and integro-differential collision operators, demonstrating their ability to accurately reproduce complex non-equilibrium plasma dynamics more effectively than traditional particle track statistics.
This paper presents an exactly solvable Path Integral Monte Carlo model for interacting harmonic fermions that reveals the sign problem is primarily inherent to the free propagator, can be analytically proven to vanish for specific closed-shell states at large imaginary time, and enables high-accuracy ground state energy calculations for quantum dots that compare favorably with modern neural network methods.
This paper introduces a novel one-dimensional Graph Neural Network model, termed the Collision-captured Network (CN), which integrates data assimilation to efficiently and accurately simulate sea ice floe collisions and trajectories, offering a scalable alternative to traditional numerical methods for forecasting in marginal ice zones.
This paper demonstrates that physics-guided diffusion sampling can effectively reconstruct full spatiotemporal trajectories of gas-phase reaction kinetics governed by advection-reaction-diffusion equations and generalize to unseen parameter regimes, overcoming the limitations of existing methods that typically focus on single-snapshot reconstructions.
This paper introduces a physics-informed Latent Space Dynamics Identification (pLaSDI) framework that overcomes the computational bottlenecks of time-dependent non-local thermodynamic equilibrium (NLTE) atomic kinetics by learning explicit reduced governing equations, achieving massive speedups while ensuring physical stability and accuracy in predicting plasma charge-state evolution for EUV lithography applications.
This paper introduces the spatial disorder-generalized Langevin equation (SD-GLE), a data-driven variational Bayesian method that successfully separates static spatial disorder from viscoelastic friction to accurately model anomalous diffusion and long-time dynamics in heterogeneous systems where standard approaches fail.
This paper introduces the "bubble method," a first-principles approach that overcomes end-point singularities in alchemical free energy calculations to accurately predict solvation free energies for molecules and ions using classical, ab initio, and machine learning molecular dynamics without relying on experimental data.
This paper develops and evaluates probabilistic extensions of Physics-Informed Neural Networks (PINNs), specifically Bayesian inference, Monte Carlo dropout, and repulsive deep ensembles, to provide reliable uncertainty quantification for inverse turbulent flow problems, demonstrating that Bayesian methods yield the most consistent estimates while repulsive ensembles offer a computationally efficient alternative.
This paper presents Scalable DDPM-Polycube, an enhanced diffusion-based method that improves the automation and robustness of polycube construction for complex CAD geometries by introducing a new blind-hole primitive, expanding to a 3D grid configuration, and implementing a hierarchical genus-guided verification strategy for all-hexahedral mesh and volumetric spline generation.