HIGHLIGHTS
SUMMARY
The deep neural_network (DNN) has achieved significant breakthroughs in various scientific and industrial fields. The cost and the difficulty of collecting data in some areas, especially in energy-related fields, hinder the develop- B ment of_(deep) neural_networks. As a successful representative, the theory-guided neural_network framework, also called a physical-informed neural_network framework or an informed deep learning framework, which incorporates the theory (e_g, governing equations, other physical constraints, engineering controls, and expert knowledge) into (deep) neural_network training, has been applied to construct the prediction model, especially in industries with limited training data . . .
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