1164 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 presents a robust and efficient hybrid overlapping moving-mesh technique integrated within the unified gas-kinetic scheme (UGKS) to accurately simulate unsteady multiscale flows with moving boundaries, such as hypersonic multi-body separation and MEMS flows, by extending implicit solvers to mitigate CFL constraints and optimize computational performance.
This paper proposes a constrained convex optimization framework for reconstructing reaction-channel levels on fixed subgroup supports that guarantees physical nonnegativity by retaining low-order channel information exactly while fitting remaining conditions via weighted least squares, thereby resolving nonnegativity violations in cross-section-space probability tables at a manageable cost to response-level accuracy.
This paper introduces a sign-blocking method that mitigates the fermion sign problem by applying data blocking during post-processing to infer energy through correlations with sign factors, achieving exceptional agreement with state-of-the-art benchmarks in the 2D Fermi-Hubbard model.
This paper introduces a multi-fidelity contextual bandit strategy combined with a three-tier DFT validation funnel to efficiently screen oxide semiconductor dopants, successfully identifying optimal Cu-containing co-doped ZnO systems for visible-light applications while reducing computational costs by 81% and revealing that dopant performance is governed by just two latent chemical dimensions.
This paper introduces the continuous Motif Symmetry-Breaking Index (MSBI) to quantify -symmetry breaking in altermagnets, enabling a DFT-free, machine-learning-driven design framework that identifies new materials with giant spin splitting, such as square-planar FeS, by optimizing symmetry, packing, and covalency descriptors.
This paper introduces a multiscale generative sampler that combines conditional Gaussian mixture models and masked continuous normalizing flows to overcome critical slowing down in lattice field theories, achieving significantly reduced autocorrelation times and enabling unbiased Multilevel Monte Carlo variance reduction for the two-dimensional scalar theory near criticality.
This study demonstrates through experiments and simulations that dynamic two-phase flow in porous media induces chaotic mixing via exponential fluid stretching, significantly enhancing solute mixing compared to steady single-phase flows by balancing shear deformation with flow reorientation at an optimal rate.
CovAngelo is a hybrid quantum-classical platform that utilizes a novel QM/QM/MM embedding model and quantum-information metrics to accurately and scalably model ligand-protein binding reactions, such as the covalent docking of zanubrutinib, while demonstrating potential speedups on current and future quantum hardware to improve drug discovery efficiency.
This paper introduces a physics-informed synthetic dataset and a U-Net-based deep learning model that successfully denoises TIE-reconstructed phase maps of transient compressible gas flows, achieving significant improvements in signal quality and structural sharpness through zero-shot generalization to real experimental data.
This paper proposes a unified sharp-diffusive phase-field model that incorporates a localized interfacial source term to independently control interface toughness and accurately simulate both bulk and interfacial cohesive fracture without requiring complex corrections or fine mesh refinement.