Deep Neural Networks (DNN) enabled Cyber-Physical Systems (CPS) hold great promise for revolutionizing many industries, such as drones and self-driving cars. However, the current generation of DNN cannot provide analyzable behaviors and verifiable properties that are necessary for safety assurance. This critical flaw in purely data-driven DNN sometimes leads to catastrophic consequences, such as vehicle crashes linked to self-driving and driver-assistance technologies. On the other hand, physics-model-based engineering methods provide analyzable behaviors and verifiable properties, but do not match the performance of DNN systems. These considerations motivate the work in this project which aims at physics-model-based neural networks (NN) redesign, yielding HyPhy-DNN: a hybrid self-correcting physics-enhanced DNN framework. HyPhy-DNN will provide the performance of DNNs and the analyzability and verifiability of physical models, thus providing a foundation for verifiably safe self-driving cars, drones, and other CPS systems. Moreover, the HyPhy-DNN will fundamentally advance the integration of deep learning and robust control to enable safety- and time-critical CPS to safely operate with high performance in unforeseen and dynamic environments.

The HyPhy-DNN will make three innovations in redesigning NN architecture: (i) Physics augmentations of NN inputs for directly capturing hard-to-learn physical quantities and embedding Taylor series; (ii) Physics-guided neural network editing, such as removing links between independent physics variables or fixed weights on links between certain physics variables to maintain the known physics identity such as in conservation laws; and (iii) Time-frequency-representation filtering-based activations for filtering out noise having dynamic frequency distribution. The novel architectural redesigns will empower the HyPhy-DNN with four targeted capabilities: 1) controllable and provable model accuracy; 2) maximum avoidance of spurious correlations; 3) strict compliance with physics knowledge; and 4) automatic correction of unsafe control commands. Finally, the safety certification of any DNN will be a long-term challenge. Hence, the HyPhy-DNN shall have a simple but verified backup controller for guaranteeing safe and stable operation in dynamic and unforeseen environments. To achieve this, the research team will integrate HyPhy-DNN with an adaptive-model-adaptive-control (AMAC) framework, the core novelty of which lies in fast and accurate nonlinear model learning via sparse regression for model-based robust control. The HyPhy-DNN and AMAC are complementary and will be interactive at different scales of system performance and functionalities during the safety-status-cycle, supported by the Simplex software architecture, a well-known real-time software technology that tolerates faults and allows online control system upgrades.