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 utilizes a newly developed Light-Matter DMRG algorithm to investigate a generalized Dicke-Ising model in high-Q cavities, revealing how cavity-induced multimode structures can optimize spin entanglement, drive transitions to superradiant phases, and stabilize quantum spin nematic states for potential quantum state engineering.
This paper presents a robust, high-resolution Eulerian algorithm for simulating high-speed polydisperse granular multiphase flows by coupling compressible gas dynamics with mass-based moment equations closed via the generalized quadrature method of moments, demonstrating its effectiveness through various complex shock-driven numerical experiments.
By training an autoregressive conditional diffusion model on Kolmogorov flow simulations, this study reveals a state-dependent hierarchy in the predictability of turbulence extremes, demonstrating that forecast horizons are governed by the persistence of large-scale coherent structures and the lifetime of pre-extreme strain cores.
This paper introduces a weighted graph model (FIU) where information-driven topology evolution governed by spectral curvature exhibits a sharp phase transition at a critical coupling strength, revealing a stable discrete Poisson relation between curvature and information flux that spontaneously demonstrates dimensional sensitivity through distinct system-size collapse thresholds in 2D versus 3D lattices.
This paper presents a 3D topology optimization framework for designing manufacturable blazed metasurface gratings that achieve high broadband diffraction efficiency in the visible and near-infrared spectrum by transitioning from complex freeform structures to fabrication-constrained pillar-based parameterizations compatible with e-beam lithography and reactive ion etching.
This paper introduces a framework combining the mapping approach to surface hopping (MASH) with transition-path sampling to efficiently simulate rare nonadiabatic reactions by generating unbiased reactive pathways that enable the detailed analysis of reaction mechanisms and rate constants.
This paper establishes a first-principles theory of vacuum Wannier functions that unifies tight-binding and nearly-free-electron models to enable predictive photoemission calculations without semiempirical potentials by constructing dense k-space scattering states at solid-vacuum interfaces.
The paper introduces auto-WHATMD, an automated algorithm that utilizes optimal transport distance and simulated annealing to efficiently identify key residues distinguishing high-dimensional molecular dynamics trajectories of protein systems, thereby enabling quantitative comparison and correlation with ligand-binding affinities without relying on arbitrary domain assumptions.
This paper introduces Excited Pfaffians, a generalized neural network architecture combined with Multi-State Importance Sampling, which enables the efficient and accurate representation of multiple excited states and potential energy surfaces with nearly constant computational cost, achieving significant speedups and scalability for systems like the carbon dimer and beryllium atom.
This paper introduces a robust, real-time machine learning framework using a one-dimensional convolutional neural network to efficiently and accurately analyze Nitrogen-Vacancy center ODMR spectra, outperforming conventional nonlinear fitting in speed and reliability—particularly at low signal-to-noise ratios—as demonstrated in intracellular temperature sensing and superconducting vortex imaging.