Phy-Taylor: Physics-Model-Based Deep Neural Networks

@article{Mao2022PhyTaylorPD,
  title={Phy-Taylor: Physics-Model-Based Deep Neural Networks},
  author={Yanbing Mao and Lui Raymond Sha and Huajie Shao and Yuliang Gu and Qixin Wang and Tarek F. Abdelzaher},
  journal={ArXiv},
  year={2022},
  volume={abs/2209.13511}
}
Purely data-driven deep neural networks (DNNs) applied to physical engineering systems can infer relations that violate physics laws, thus leading to unexpected consequences. To address this challenge, we propose a physics-model-based DNN framework, called Phy-Taylor, that accelerates learning compliant representations with physical knowledge. The Phy-Taylor framework makes two key contributions; it introduces a new architectural physics-compatible neural network (PhN), and features a novel… 

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