24-081 DJSS - 24-081 GPU Server for Learning-based Control of Cyber-Physical Systems

The main research objectives are: 1) to build a GPU-enabled modelling framework for "pixel-to-action" control of safety-critical CPS using Koopman-Zubov operator learning; 2) to develop GPU-accelerated computational physics-informed neural networks (PINNs) for CPS control; 3) to test the GPU-in-the-loop modelling and control framework with real robotic platforms involving both moderate-scale ground robots and teams of UAVs. It is expected that the outcomes of the proposed research will significantly advance the state-of-the-art in safety-critical control of CPS with formal guarantees and will train highly qualified personnel (HQP) for Canada's CPS sectors, including robotics, automotive, and autonomous systems.