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Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamics
The proposed method is based on the systematic encoding of physics-based prior knowledge into a structured recurrent neural architecture.
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