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physics.comp-ph2021
Physics-Informed Machine Learning for Optical Modes in Composites
Abantika Ghosh, Mohannad Elhamod, Jie Bu +3
We demonstrate that embedding physics-driven constraints into machine learning process can dramatically improve accuracy and generalizability of the resulting model. Physics-inform…
physics.comp-ph2018★ 13 cited
Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
Xiaowei Jia, Jared Willard, Anuj Karpatne +4
This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the mo…