1201 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 analyzes the accuracy of the standard Yee FDTD scheme for normal incidence on dielectric and magnetic interfaces by deriving discrete Fresnel coefficients, quantifying systematic errors caused by the grid's implicit transition layer, and providing rigorous error estimates and qualitative criteria to guide simulation practice and validate structure-preserving discretizations.
This paper elucidates the mechanism behind the high transition temperature in sulfur superhydride HS by demonstrating that its valence states are plane-wave-like, leading to a density-of-states peak near the Fermi level through the hybridization of specific plane waves driven by the adjacency of Jones' large zone to the Fermi surface.
This paper presents an extended PtychoPINN framework that enables overlap-free, single-shot coherent diffractive imaging and accelerates conventional ptychography by coupling a differentiable forward model with a Poisson likelihood, thereby achieving high-throughput, dose-efficient reconstructions validated on experimental data from synchrotron and XFEL sources.
This paper proposes and validates a physics-informed neural network (PINN) framework, benchmarked against an analytical solution, to accurately model the dispersion of SH-waves in a pre-stressed, gravity-influenced, functionally graded magnetoelastic layered composite structure.
This paper demonstrates that the previously developed imaginary-time multi-electronic-state path integral (MES-PI) formulation naturally captures the geometric phase effect arising from conical intersections, and quantifies its impact on low-temperature thermodynamic properties using an ad hoc GP-excluded construction as a comparison baseline.
This paper demonstrates that conventional machine learning interatomic potentials fail to accurately model mixed-valence materials like NaFePO4 due to their inability to capture electronic entropy, but introducing explicit charge-state information into the potential's representation successfully resolves these errors and enables correct structural optimization and thermodynamic predictions.
This paper demonstrates that unsaturated flow in fractured rock exhibits a distinct two-branch retention behavior driven by fracture-matrix flow partitioning, which is explained through a newly derived analytical framework identifying a critical saturation that marks the transition between matrix- and fracture-dominated regimes.
This paper demonstrates that a fully atomistic classical electrodynamic model, which couples intraband charge transport and interband polarization, quantitatively reproduces chiroptical spectra across the quantum-to-classical regime, thereby establishing a unified electrodynamic origin for plasmonic chirality and enabling the rational design of chiral plasmonic nanostructures.
This paper investigates the vacuum states and qualitative patterns of a non-local q-color Potts model on a random two-dimensional lattice through numerical simulations, exploring its conjectured relationship to the chromatic number of a plane problem.
This paper introduces a scalable Layered Local Hidden Variable Neural Network (Layered LHV-Net) framework to characterize genuine network nonlocality in the triangle scenario, revealing new robust measurement settings, stricter noise thresholds, and the resilience of nonlocal correlations against shared classical randomness, thereby demonstrating the transformative potential of quantum-informed machine learning in quantum information science.