1190 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 Boltzmann generator framework based on normalizing flows that directly maps solvent configurations between solutes of different sizes, demonstrating its ability to accurately and efficiently estimate solvation free energies for challenging transformations while significantly enhancing configurational overlap compared to conventional methods.
The paper proposes the Spatio-Temporal Uncertainty-Modulated PINN (UM-PINN), a probabilistic framework that leverages homoscedastic aleatoric uncertainty and adaptive weighting to effectively resolve strong shock waves in hyperbolic conservation laws, significantly outperforming standard Physics-Informed Neural Networks in accuracy and shock resolution.
This paper introduces the Alternating Cross Interpolation (ACI) algorithm, which accelerates elementwise operations on tensor trains from to while preserving error control, offering significant speedups for practical applications.
The paper introduces PhysVEC, a multi-agent framework that leverages programming and scientific verifiers to ensure code correctness and physical validity, significantly improving the reliability and accuracy of LLM-driven automated discovery in quantum many-body physics as demonstrated on a new benchmark dataset.
This paper presents a novel numerical methodology using an ultraweak variational formulation of the envelope Maxwell model, discretized by the discontinuous Petrov-Galerkin (DPG) method with specialized perfectly matched layers, to accurately extract optical field losses in 3D circularly coiled waveguides as a boundary value problem, achieving stable convergence for the first time with this specific approach.
This paper introduces a geometry-informed neural atlas that replaces traditional volumetric meshing with a learned, differentiable representation of complex 3D geometries, enabling efficient forward and inverse boundary value problem solving across diverse discretization methods without re-meshing.
This paper introduces Predictor-Driven Diffusion, a novel framework that integrates renormalization-group-based spatial coarse-graining with path-integral temporal dynamics to enable a unified predictor for spatiotemporal generation, simulation, and super-resolution in multiscale turbulent systems.
This paper introduces a measure-valued neural network that learns measure-dependent interaction forces directly from particle trajectories to model McKean-Vlasov dynamics, supported by theoretical guarantees on well-posedness and universal approximation, and validated through diverse numerical experiments demonstrating accurate prediction and strong generalization.
This paper introduces SAT3-NN, a neural network framework trained on high-resolution CGYRO simulation data that outperforms previous quasilinear saturation models (SAT0–SAT3) by more accurately predicting turbulent fluxes and capturing anti-gyroBohm scaling from linear gyrokinetic inputs.
The paper introduces BuSyNet, a deep learning architecture that integrates dimensional consistency and symplectic geometry to discover interpretable, closed-form symbolic Hamiltonian expressions, achieving superior long-term prediction accuracy and stability on physical systems like the harmonic oscillator and Kepler problem compared to state-of-the-art methods.