Workshop on Physics-Informed Machine Learning in Control:
An Introduction, Opportunities and Challenges

In recent years, research at the intersection of machine learning and classical engineering domains has grown exponentially. Machine learning is increasingly being utilized to develop novel data-driven approaches for modeling and controlling dynamical systems, which were traditionally dominated by physics-based models and scientific computing solvers. Conversely, principles from engineering and scientific computing are transforming the machine learning landscape by introducing domain-aware methods, moving beyond purely black-box approaches, and incorporating more structure and prior knowledge into model architectures and loss functions.

Physics-informed machine learning leverages knowledge of the physical world to guide the learning processes of machine learning algorithms. By explicitly integrating physical laws, domain expertise, and prior knowledge into the learning framework, physics-informed learning enables control systems to benefit from the flexibility and adaptability of machine learning while maintaining a strong foundation in understanding the underlying dynamics. This integration results in more efficient and trustworthy learning processes, ultimately leading to superior performance, robustness, and interpretability control applications.

This workshop aims to provide insight into the latest advances in physics-informed machine learning for modeling, control, and optimization, and to highlight open challenges and opportunities in the field. In the morning, experts with experience in physics-informed learning and optimization-based control will present new results, address challenges and opportunities for the control community, and discuss recent advances in physics-informed learning more broadly. In the later afternoon, a tutorial-style coding session will offer attendees hands-on experience with tools from the physics-informed learning ecosystem.

The workshop targets an audience from graduate level to experienced theoretical and practically oriented control engineers who aim to improve their knowledge in physics-informed machine learning for control and optimization.