The College of William & Mary received a $229,986 Project Grant award from the National Science Foundation Division of Electrical, Communications and Cyber Systems Engineering program (CFDA 47.041) to develop a hybrid physics-enhanced deep neural network (HyPhy-DNN) framework. The HyPhy-DNN aims to provide the performance benefits of deep neural networks with the analyzability, verifiability, and safety properties of physical models. It will incorporate three architectural innovations: physics augmentations of inputs, physics-guided network editing, and time-frequency representation filtering-based activations. The HyPhy-DNN framework seeks to enable verifiably safe autonomous vehicles and other cyber-physical systems by providing controllable model accuracy, avoiding spurious correlations, complying with physics knowledge, and automatically correcting unsafe control commands. It will be integrated with an adaptive-model adaptive-control framework to guarantee safe operation in dynamic environments through a simple verified backup controller. The three-year award was issued on June 15, 2023 to support development of this hybrid physics-enhanced neural network approach for learning and control of cyber-physical systems.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $230.0k | 6/8/23 |