Speaker
Description
Machine learning has emerged as a powerful tool for modeling and controlling complex dynamical systems, yet many approaches overlook structural properties with a long-standing history in systems and control theory. This plenary highlights how symmetry, invariants, Hamiltonian structure, and optimality conditions can be embedded into learning architectures for dynamical systems and optimal control. Enforcing such structure through model design, loss functions, and constraints leads to methods that are data-efficient, interpretable, and compatible with control objectives. Examples include symmetry-aware and Hamiltonian neural networks as well as optimality-informed learning for parametric control problems. Overall, the perspective bridges model-based and data-driven approaches, showing how machine learning and control theory can mutually inform and advance one another.
