Neuro-symbolic Generative AI Assistant for System Design

The design of complex cyber-physical systems involves balancing multiple, often conflicting performance objectives. In practice, some design requirements remain implicit, embedded in the intuition and expertise of seasoned designers who have worked on similar systems for years. These designers rely on their experience to explore a limited set of promising design candidates, evaluating or simulating them with detailed but computationally slow scientific models. The typical goal is to produce a diverse array of high-performing configurations that offer flexibility in trade-offs and avoid premature commitment to a specific design. In this invited talk, we describe an AI assistant that leverages neuro-symbolic machine learning to automate parts of the system design process. Our approach extends oracle-guided inductive synthesis by integrating a hierarchy of oracles, ranging from slow, detailed scientific models to faster but lower-fidelity deep neural network surrogates and symbolic rules. This approach accelerates design iterations, especially during early design phases. We employ deep generative models in the form of fine-tuned large language models to learn the valid design space, followed by joint exploration and optimization across this learned manifold. This allows the generation of a diverse set of optimal designs based on specified performance objectives.