12 papers
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…
NOWS: Neural Operator Warm Starts for Accelerating Iterative Solvers
Mohammad Sadegh Eshaghi, Cosmin Anitescu, Navid Valizadeh +3
Partial differential equations (PDEs) underpin quantitative descriptions across the physical sciences and engineering, yet high-fidelity simulation remains a major computational bo…
Artificial intelligence for partial differential equations in computational mechanics: A review
Yizheng Wang, Jinshuai Bai, Zhongya Lin +9
In recent years, Artificial intelligence (AI) has become ubiquitous, empowering various fields, especially integrating artificial intelligence and traditional science (AI for Scien…
Towards Unified AI-Driven Fracture Mechanics: The Extended Deep Energy Method (XDEM)
Yizheng Wang, Yuzhou Lin, Somdatta Goswami +8
Physics-Informed Neural Networks (PINNs) have recently emerged as powerful tools for solving partial differential equations (PDEs), with the Deep Energy Method (DEM) proving especi…
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…