Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes

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In this paper, we introduce a novel framework for combining scientific knowledge within physics-based models and recurrent neural networks to advance scientific discovery in many dynamical systems.

• We will first describe the use of outputs from physics-based models in learning a hybrid-physics-data model.
• Then, we further incorporate physical knowledge in real-world dynamical systems as additional constraints for training recurrent neural networks.
• We will apply this approach on modeling lake temperature and quality where we take into account the physical constraints along both the depth dimension and time dimension.