21 papers
Plasolver: Physics-Informed Neural Operators for Elastoplasticity
Yizheng Wang, Mohammad Sadegh Eshaghi, Huadong Zhang +3
Elastoplastic analysis is computationally demanding because its nonlinear, path-dependent constitutive behavior requires incremental loading and repeated iterative solutions. To ad…
Neural Operators for Immersed-Boundary Soft Swimmers Locomotion
Mohammad Sadegh Eshaghi, Yizheng Wang, Navid Valizadeh +2
High-fidelity immersed-boundary simulation resolves the coupled motion of a deforming swimmer and its surrounding flow, but the resulting cost limits repeated evaluations for engin…
FEVessel: Mesh-Independent Analysis of 3D Pressure Vessels with the Label-Free Pretrained Finite Element Method
Yipin Sun, Yizheng Wang, Yuzhou Lin +3
Pressure vessel analysis in the chemical, nuclear, and new-energy industries requires solving the same elasticity problem across many materials, geometries, and loads, where mesh q…
GA-VINO: A Geometry-Aware Variational Physics-informed Neural Operator for Mindlin-Reissner Plates
Siqi Wang, Daobo Sun, Yizheng Wang +4
Plate and shell structures are widely used in engineering fields. Rapid response prediction for such structures under complex geometries, heterogeneous materials, and varying loads…
HAMNO: A Hierarchical Adaptive Multi-scale Neural Operator with Physics-Informed Learning for Dynamical Systems
Mostafa Bamdad, Mohammad Sadegh Eshaghi, Timon Rabczuk
Neural operators provide a powerful framework for learning solution mappings of partial differential equations directly in function space. However, many existing architectures stil…
Dmsh: A Multi-Agent Reinforcement Learning Framework for All-Quad Mesh Generation
Anirudh Kalyan, Cosmin Anitescu, Xiaoying Zhuang +3
Generating high-quality meshes for arbitrary geometries remains a fundamental bottleneck in computational engineering, often demanding heuristic tuning and semi-manual workflows. I…