903 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 article reviews the paradigm of data-driven scientific machine learning, specifically highlighting how neural operators offer a faster, resolution-invariant alternative to conventional numerical methods for solving partial differential equations, while also addressing their potential to complement traditional techniques and noting existing challenges.
The paper introduces LEDDS, an open-source framework that achieves high-performance, portable LBM-DEM simulations on GPUs by expressing complex fluid-particle interactions entirely through algorithmic primitives, delivering performance comparable to hand-tuned CUDA solvers while maintaining code clarity and broad hardware compatibility.
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 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 evaluates the hydrodynamic behavior and validity limits of an immersed boundary–lattice Boltzmann method for wetting problems by comparing its contact-line model and thin-film formation against boundary element and volume of fluid solvers.
This study combines computational modeling and astrochemical simulations to reveal that while amorphous solid water ice shields interstellar thiocyanates HCSCN and HCSCCH from thermal desorption through strong binding, it simultaneously creates a "survival paradox" where these deeply trapped molecules become more vulnerable to photodissociation due to enhanced UV absorption cross-sections.
This paper introduces two deep learning autoregressive surrogate models—a Koopman-based Transformer and a ConvLSTM-UNet—that accurately and efficiently predict the temporal evolution of 2D ideal magnetohydrodynamic Kelvin-Helmholtz instabilities while preserving key physical structures and invariants at a substantially reduced computational cost compared to direct numerical simulations.
This paper resolves the challenge of controlling energy dissipation in non-spherical particle contact by deriving a structure-preserving damping formulation from first principles that ensures consistent contact-point restitution through a unique damping law aligned with the system's intrinsic translational-rotational coupling.
This paper presents a physics-informed neural network framework that integrates variational learning with Magnus expansion to optimize control protocols and maximize Quantum Fisher Information in time-dependent many-body systems, demonstrating superior performance over reference solutions for up to six qubits.