Neuro-symbolic AI is often envisioned as the next generation, ‘third wave’ of AI that will offer unique advantages both in design-time (e.g., engineering effort and verifiability of results) and at run-time (e.g., performance and safety). While these claims are expected to be true for engineered systems where development processes are built on the model-based (symbolic) tradition, they have not been substantiated in convincing, scaled examples.

Widespread use of automation in many sectors of society has yielded a large amount of data regarding historical behaviors for a variety of dynamical systems, such as unmanned aerial, marine, and ground vehicles, biological systems, and weather systems. This project aims to develop novel algorithms to discover governing rules that explain the observed behaviors (i.e., trajectories) of dynamical systems. Discovery of underlying models, while useful for analysis and control, can be computationally challenging.

Cyber-physical systems (CPS) are engineered systems created as networks of interacting physical and computational processes. Most modern products in major industrial sectors, such as automotive, avionics, medical devices, and power systems already are or rapidly becoming CPS driven by new requirements and competitive pressures.