Project Grant 2311084

Award Date 6/15/23
Completion Date 5/31/26
Dollars Obligated $296K
Federal Grant Program
47.041
Assistance Type
Project Grant
Place of Performance
Detroit, MI 48202, USA
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Under this award, the university will develop a hybrid self-correcting physics-enhanced deep neural network framework called HYPHY-DNN. HYPHY-DNN aims to provide the performance benefits of deep learning models while incorporating analyzable behaviors and verifiable properties from physical models. This is intended to help ensure safety for applications involving cyber-physical systems, such as self-driving vehicles and drones. Specifically, Wayne State researchers will implement innovations in neural network architecture design, including physics augmentations of inputs, physics-guided network editing, and time-frequency representation filtering activations. It is expected this work will advance integration of deep learning and robust control methods to enable safety-critical cyber-physical systems to safely operate with high performance in dynamic environments.

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