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.
This paper demonstrates that a lightweight and scalable EfficientNet architecture, augmented with global jet features, achieves competitive top-quark jet tagging performance while significantly reducing computational costs compared to Transformer-based and standard Graph Neural Network approaches.
The paper introduces MAD-SURF, a machine learning interatomic potential trained on diverse datasets that achieves density functional theory-level accuracy for simulating organic molecule adsorption on coinage metal surfaces while enabling computational speeds orders of magnitude faster.
This paper demonstrates that the spectral complexity of the radiative transfer equation admits a finite effective rank via Young-measure homogenization, enabling highly accurate and efficient low-rank tensor train decompositions that significantly outperform traditional approximations like the correlated-k distribution across diverse molecular and atomic opacity sources.
This paper introduces Inelastic Constitutive Kolmogorov-Arnold Networks (iCKANs), a novel machine learning framework that automatically discovers interpretable, closed-form symbolic constitutive laws for both elastic and inelastic material behaviors by translating experimental data into potential functions, as demonstrated on viscoelastic polymers.
This study demonstrates that an optimized higher-order harmonic surface tessellation, developed through an analytical and numerical framework, outperforms conventional gyroid TPMS structures in turbulent flow regimes by achieving a superior balance of high thermal effectiveness and lower pressure drop, with secondary surface wave frequency identified as the critical design parameter.
The paper introduces PINEAPPLE, a novel framework combining physics-informed neural networks with evolutionary search to enable rapid, accurate, and robust real-time inference of internal lithium-ion battery electrode parameters solely from voltage-time discharge curves, facilitating non-destructive state-of-health diagnostics for next-generation battery management systems.
This paper reviews and demonstrates a scalable, symmetry-aware machine learning force-field framework that accurately predicts electron-mediated forces for large-scale Landau-Lifshitz-Gilbert simulations of itinerant magnets, revealing novel nonequilibrium spin dynamics phenomena.
This paper establishes a robust electrodynamic framework using causal dielectric functions and full multipolar convergence to demonstrate that the net transverse linear momentum transferred from a swift electron to a large spherical nanoparticle is consistently attractive, thereby correcting previous theoretical predictions of repulsive behavior and highlighting the need for additional physical mechanisms to explain experimental observations.
This paper introduces a computationally efficient, open-source method that utilizes a matrix-valued adaptive Antoulas-Anderson (AAA) algorithm to represent the frequency-dependent T-matrix as a pole-expansion, thereby overcoming the high memory and computational costs of traditional discrete frequency sampling while preserving physical interpretability.
This paper benchmarks the nested_fit program, which utilizes nested sampling and slice sampling, by successfully determining the partition functions and identifying phase transitions and stable configurations for 7-atom and 36-atom Lennard-Jones clusters while highlighting the algorithm's impact on computational cost.