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 a multiphysics computational model for the Molten Salt Fast Reactor using the Cardinal framework to integrate NekRS for thermal hydraulics with the Fission Matrix method for efficient neutronics, replacing the standard OpenMC solver to enable fast and accurate simulations of the tightly coupled fuel-coolant system.
The paper introduces Scale-PINN, a novel learning strategy that integrates the iterative residual-correction principle of numerical solvers into Physics-Informed Neural Networks to drastically reduce training time and improve accuracy, thereby bridging the gap between deep learning and traditional numerical methods for practical scientific applications.
This paper proposes a Bayesian estimation method to optimize the measurement window in synchrotron-radiation-based Mössbauer spectroscopy, demonstrating that this approach improves the precision of center shift measurements by more than three times compared to conventional Lorentzian fitting.
This paper introduces a reinforcement learning-based framework that effectively characterizes and emulates quantum hardware noise to bridge the gap between simulations and real superconducting qubit execution, offering greater flexibility than conventional modeling techniques.
This paper introduces a novel amortized inference technique using likelihood-weighted Normalizing Flows that overcomes the limitations of standard unimodal base distributions in capturing multi-modal posteriors by initializing the flow with a Gaussian Mixture Model, thereby enabling efficient and accurate parameter estimation in high-dimensional inverse problems without requiring posterior training samples.
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 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.