• Corpus ID: 238259059

CENN: Conservative energy method based on neural network with subdomains for solving heterogeneous problems involving complex geometries

@article{Wang2021CENNCE,
  title={CENN: Conservative energy method based on neural network with subdomains for solving heterogeneous problems involving complex geometries},
  author={Yizheng Wang and Jia Sun and Xiang Li and Yinghua Liu},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.01359}
}
We propose a conservative energy method based on a neural network with subdomains (CENN), where the admissible function satisfying the essential boundary condition without boundary penalty is constructed by the radial basis function, particular solution neural network, and general neural network. The loss term at the interfaces has the lower order derivative compared to the strong form PINN with subdomains. We apply the proposed method to some representative examples to demonstrate the ability… 

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Physics-informed machine learning
TLDR
Some of the prevailing trends in embedding physics into machine learning are reviewed, some of the current capabilities and limitations are presented and diverse applications of physicsinformed learning both for forward and inverse problems, including discovering hidden physics and tackling highdimensional problems are discussed.
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