xiaoying Zhuang

Abstract Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especially effective in fracture mechanics due to its energy-based formulation. Despite these advances, existing DEM approaches require dense collocation near cracks, face stability challenges, and typically treat discrete and continuous fracture models separately.

[Submitted on 9 Jun 2026] Title:Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation View a PDF of the paper titled Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation, by Anirudh Kalyan and 5 other authors View PDF HTML (experimental) Abstract:Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows.

1 INTRODUCTION Flexoelectricity—an electromechanical coupling between polarization and strain gradient-exists in all dielectric materials. Moreover, flexoelectricity is a size-dependent effect that becomes more significant in nanoscale systems.1 These features make it different from piezoelectricity, which is induced polarization due to homogeneous strain.