922 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 study demonstrates that irradiating collinear ferromagnetic helical structures with polarized light induces spin-split transmission and suppresses thermal conductance, thereby significantly enhancing spin thermoelectric performance and the figure of merit, particularly when combined with long-range hopping.
This paper compares projection-based and slip-gradient methods for estimating geometrically necessary dislocation (GND) densities in crystal plasticity finite element models, revealing that while both align with analytical trends, the projection method significantly underestimates GNDs in polycrystals unless it is improved by restricting calculations to only active dislocation systems.
The paper introduces ELECTRA, an equivariant Cartesian tensor network that leverages floating Gaussian orbitals to accurately predict 3D electronic charge densities and significantly accelerate DFT convergence by learning optimal orbital placements in a data-driven manner.
This paper presents a novel Gaussian process-based framework that learns probabilistic Dirichlet-to-Neumann maps on graphs by integrating discrete exterior calculus and nonlinear optimal recovery to enforce conservation laws, thereby enabling accurate, uncertainty-quantified predictions in data-scarce multiphysics applications like subsurface fracture networks and arterial blood flow.
The paper introduces LEGO-xtal, a symmetry-informed AI generative framework that rapidly produces diverse crystal structures matching a target local environment by combining AI-generated initial structures with machine learning-based optimization, successfully expanding a small set of carbon allotropes into over 1,700 viable candidates.
This paper presents a novel local framework for identifying three-dimensional magnetic reconnection X-lines and estimating reconnection rates in turbulent plasmas by applying fluid visualization bifurcation lines and magnetic shear layer measurements, offering an efficient alternative to traditional global methods across various simulation models.
This paper presents a Metropolis Monte Carlo algorithm capable of handling complex phase space weights in quantum statistical mechanics and demonstrates its accuracy by successfully calculating the saturation liquid density of He near the -transition using a third-order Wigner-Kirkwood expansion.
This paper presents a semi-analytical minimal framework using a parametrized hyperbolic Ansatz to efficiently model vortex nucleation and magnetization reversal in spherical magnetic nanoparticles, successfully deriving analytical estimates for critical nucleation parameters that extend Brown's classic results.
This paper demonstrates through direct numerical simulation and experimental comparison that the wake flow behind a thin two-dimensional flat plate becomes fully turbulent at a relatively low Reynolds number of 400, exhibiting statistical and spectral characteristics indistinguishable from higher-Reynolds-number turbulent wakes, a transition path that differs significantly from that of canonical circular or square cylinders.
This paper utilizes the Binary Intrinsic Dimension (BID) to characterize the phases and transitions of the Hopfield model, demonstrating its robustness against finite-size effects and revealing a direct link between the geometry of the state space and standard spin order parameters.