1258 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 Drifting Models for molecular conformation generation, leveraging a novel Drifting Force Identity to incorporate physical force labels into one-step sampling, thereby achieving million-fold acceleration over traditional molecular dynamics while maintaining perfect structural validity and Boltzmann distribution accuracy.
The paper introduces JAWS, a probabilistic regularization strategy that dynamically modulates Jacobian constraints based on local physical complexity to resolve the contraction-dissipation dilemma, thereby enabling memory-efficient, short-horizon optimization to achieve superior long-term stability and accuracy in neural operator rollouts for dynamical systems.
This study develops and validates a combined analytical and numerical framework linking thermal spray nozzle operating conditions to far-field aeroacoustic signatures and in-flight particle transport, demonstrating that acoustic monitoring can serve as a non-intrusive method for controlling and optimizing the thermal spray process.
This paper demonstrates that the Continuous-Time Koopman Autoencoder (CT-KAE) serves as a lightweight, stable, and efficient surrogate model for long-horizon ocean state forecasting, outperforming autoregressive Transformer baselines by maintaining bounded errors and consistent large-scale statistics over 2083-day rollouts while enabling resolution-invariant predictions.
This paper introduces a novel hybrid ensemble forecasting framework that uses spectral nudging to integrate machine-learned large-scale guidance with physics-based mesoscale dynamics, resulting in significant forecast skill improvements of up to two days in the tropics and enhanced tropical cyclone track predictions without compromising storm intensity or ensemble spread.
This paper demonstrates that pretraining tokenizers with an autoencoding objective before training dynamics models significantly enhances the computational efficiency and accuracy of physics foundation models, particularly when the pretraining data aligns with the downstream physical system.
This paper introduces TRec, a physics-informed muon scattering tomography framework that significantly enhances the detection of missing fuel in sealed microreactor cores by reconstructing curved muon trajectories and incorporating momentum measurements, thereby outperforming conventional methods like PoCA in both sensitivity and speed under realistic cosmic-ray conditions.
This paper introduces and validates new explicit fourth-order Multistep Runge-Kutta (MSRK) methods that accelerate Numerical Relativity simulations by reusing data from previous time steps to reduce intermediate stage evaluations, while providing a procedure to tune coefficients for maximizing stable time step sizes.
This paper introduces Simulated Bifurcation-based Configuration Interaction (SBCI), a classical mechanics-inspired algorithm that significantly reduces the computational cost of high-accuracy electronic structure calculations while maintaining reliability comparable to standard methods.
This study demonstrates that a machine learning-based mesh movement approach (UM2N) integrated into the Thetis software significantly accelerates and robustly enhances the accuracy of non-hydrostatic tsunami simulations for probabilistic coastal hazard assessment.