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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…
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…
WINO: A Weak-Form Physics Informed Neural Operator for Hyperelasticity on Variable Domains
Bokai Zhu, Qinghui Zhang, Yizheng Wang +1
We propose a Weak-form Physics-Informed Neural Operator (WINO), a data-free framework that combines the efficiency of neural operators with the geometric flexibility of the -fi…
Pretrain Finite Element Method: A Pretraining and Warm-start Framework for PDEs via Physics-Informed Neural Operators
Yizheng Wang, Zhongkai Hao, Mohammad Sadegh Eshaghi +4
We propose a Pretrained Finite Element Method (PFEM),a physics driven framework that bridges the efficiency of neural operator learning with the accuracy and robustness of classica…
Deep Energy Method with Large Language Model assistance: an open-source Streamlit-based platform for solving variational PDEs
Yizheng Wang, Cosmin Anitescu, Mohammad Sadegh Eshaghi +3
Physics-informed neural networks (PINNs) in energy form, also known as the deep energy method (DEM), offer advantages over strong-form PINNs such as lower-order derivatives and few…