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 paper introduces a novel physics-based active learning algorithm that leverages partial differential equation residuals to guide data selection, significantly improving the data efficiency of neural operator training for solving partial differential equations while injecting physics inductive bias into the process.
This study presents a comprehensive numerical framework based on self-consistent phonon renormalization and fourth-order anharmonicity to accurately model the temperature-dependent thermal and thermodynamic properties of both highly and weakly anharmonic insulators, overcoming the limitations of traditional perturbative approaches.
This paper establishes the first non-asymptotic oracle complexity bounds for annealed importance-based normalizing constant estimation without relying on isoperimetric assumptions and proposes a novel reverse diffusion sampler to overcome the limitations of traditional geometric interpolation in multimodal settings.
This paper introduces a transparent, encoding-agnostic framework that uses resource counts and hardware benchmarks to demonstrate that achieving early quantum utility for the Capacitated Vehicle Routing Problem (CVRP) is currently unlikely on NISQ devices, revealing a massive qubit advantage for higher-order encodings over direct QUBO mappings while suggesting that innovative problem decomposition is essential for future quantum advantage.
This paper introduces a data-driven framework that models microstructure as a stochastic process to construct a low-dimensional, invertible material manifold, successfully linking processing conditions to microstructural outcomes and enabling accelerated, closed-loop materials design.
This paper introduces a kinetic closure for the filtered Boltzmann--BGK equation that models turbulent subfilter effects through a generalized collision operator without requiring scale separation or a Smagorinsky-type ansatz, demonstrating improved stability and reduced dissipation in numerical tests compared to classical approaches.
This paper introduces the Walsh-Hadamard Neural Operator (WHNO), a novel architecture utilizing Walsh-Hadamard transforms to effectively solve partial differential equations with discontinuous coefficients by overcoming the limitations of Fourier-based methods, and demonstrates that combining WHNO with Fourier Neural Operators in an ensemble yields significantly superior accuracy in capturing both sharp interfaces and smooth features.
The paper introduces SCULPT, an interactive web-based machine learning platform that utilizes advanced techniques like UMAP and adaptive confidence scoring to analyze high-dimensional multi-particle coincidence data from COLTRIMS experiments, thereby enabling efficient discovery of rare events and correlations in atomic and molecular physics.
NORi is a novel, physics-based machine learning parameterization that combines neural ordinary differential equations with a Richardson number-dependent closure to accurately and stably simulate ocean boundary layer turbulence and entrainment dynamics in climate models, outperforming traditional methods while requiring minimal training data and ensuring long-term numerical stability.
This paper demonstrates that network topology serves as a reconfigurable design parameter for task-specific computation, enabling the programmable control of chaotic dynamics in neural circuits through edge rewiring to optimize reservoir computing performance.