1259 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.
The paper introduces the Eggbox Ising model, a tunable framework for constructing rugged energy landscapes with controllable replica-symmetry-breaking structures and Parisi overlap distributions that exhibit discontinuous finite-temperature transitions, metastability, and hysteresis.
The paper introduces GollumFit, an open-source framework designed to efficiently perform binned-likelihood analyses on IceCube neutrino telescope data by incorporating both general and specific model parameters to handle complex fitting scenarios with numerous nuisance parameters.
This paper quantifies the impact of various Geant4 electromagnetic physics constructors on the accuracy of energy deposition and computational performance for rare event searches using CaWO and Ge targets, aiming to guide the selection of optimal simulation configurations for background prediction.
This paper introduces a novel nested sampling method that treats temperature as an additional parameter within an extended partition function, enabling the efficient calculation of thermodynamic properties for temperature-dependent potentials in a single run and overcoming the computational inefficiency of traditional temperature-by-temperature approaches.
This paper proposes an instance-wise adaptive sampling framework that dynamically constructs compact, tailored training datasets for inverse problems, significantly improving sample efficiency and accuracy compared to conventional fixed-dataset approaches, particularly in scenarios with complex priors or high precision requirements.
This paper presents a distillation framework that compresses complex, state-of-the-art eSPA ensemble forecasts of ENSO phase into compact, interpretable models, thereby preserving high predictive skill while enabling rigorous diagnostics of the spatiotemporal dynamics and physical precursors driving long-range ENSO predictability.
This paper introduces QHFlow2, a state-of-the-art machine learning Hamiltonian model that significantly outperforms existing methods in energy and force prediction accuracy by directly evaluating predicted Hamiltonians, achieving NequIP-level force precision and up to 20-fold improvements in energy error on standard benchmarks.
This study combines order-of-magnitude analysis of Reynolds-averaged Navier-Stokes equations with data-driven symbolic regression to derive compact, interpretable, and physically constrained correlations for turbulent pipe friction factors that accurately fit experimental data across a wide range of Reynolds numbers and roughness levels.
This paper demonstrates that flow matching in a lower-dimensional latent space, enhanced by explicit or implicit geometric regularization, enables fast, single-step stochastic closure modeling for complex dynamical systems like 2D Kolmogorov flows while preserving physical fidelity and requiring minimal training data.
This study systematically compares human judgments and Large Language Model classifications across 22 morally ambiguous scenarios to investigate how AI systems operationalize the concept of violence, revealing their role in shaping public reasoning about harm and social norms.