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 proposes a unified framework that bridges geometric deep learning and rigorous numerical analysis for CFD simulations by introducing multi-node prediction, temporal correction via cross-attention, and 3D rotary positional embeddings to overcome the stability and accuracy limitations of existing ML surrogates on unstructured meshes.
This paper proposes a Physics-Guided Deep Learning approach using a U-Net architecture to effectively suppress structured, non-stationary artifacts in single-shot X-ray imaging, significantly improving reconstruction quality and signal preservation compared to traditional methods while incorporating deep ensembles to ensure robustness through uncertainty estimation.
This paper introduces a composition-weighted symbolic regression framework that combines hybrid search algorithms with max/min operators to generate interpretable, analytical expressions for predicting diverse materials properties directly from chemical composition, achieving competitive accuracy against black-box models while revealing chemically meaningful elemental trends.
This paper proposes and validates a strategy for constructing explicit functionals that directly map Kohn-Sham potentials to charge densities in inhomogeneous materials using homogeneous electron gas data, successfully demonstrating improved accuracy through increasingly sophisticated approximations without solving the Kohn-Sham Schrödinger equation.
This study demonstrates that the vorticity packing fraction in decaying two-dimensional Navier-Stokes turbulence governs the transition from point-vortex to finite-size vortex equilibria, which in turn dictates a corresponding shift in Lagrangian tracer transport from sub-diffusive orbital trapping to super-diffusive linear motion as packing increases.
This contribution presents a scale-sensitive adversarial analysis framework via constrained diffusion decomposition, demonstrating that standard generative AI models do not internalize physical laws across scales but instead exhibit structural freezing and instability under physically constrained perturbations.
This paper introduces MuDirac 1.3.0, a sustainable and efficient open-source software tool that enables the negative muon community to accurately calculate nuclear properties, such as the charge radius, by modeling muonic X-ray transition energies under a two-parameter Fermi distribution.
This paper presents an experimental hybrid spatio-temporal photonic reservoir computer using a diffractively coupled VCSEL array that significantly enhances classification performance and scalability by combining spatial coupling with time multiplexing to expand a 12-node network into a 968-node system with a reduced test error of 0.026.
This paper proposes an efficient algorithm based on the MeLoCoToN approach that formulates integer factorization as a tensor network equation derived from a binary multiplication circuit, optimizing the network structure and demonstrating its performance through exact and approximate contraction methods.
This paper introduces the xARPES Python code, which utilizes an extended maximum-entropy method with Bayesian inference to consistently extract electron self-energies and Eliashberg functions from curved dispersions in angle-resolved photoemission spectroscopy data, demonstrating superior accuracy on both model and experimental datasets compared to existing linearization-based approaches.