• Corpus ID: 218674320

A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks

@article{Ma2020ACD,
  title={A Combined Data-driven and Physics-driven Method for Steady Heat Conduction Prediction using Deep Convolutional Neural Networks},
  author={Hao Ma and Xiangyu Y. Hu and Yuxuan Zhang and Nils Thuerey and Oskar J. Haidn},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.08119}
}
With several advantages and as an alternative to predict physics field, machine learning methods can be classified into two distinct types: data-driven relying on training data and physics-driven using physics law. Choosing heat conduction problem as an example, we compared the data- and physics-driven learning process with deep Convolutional Neural Networks (CNN). It shows that the convergences of the error to ground truth solution and the residual of heat conduction equation exhibit… 

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