Workshop on Physics-informed Learning for Control and Optimization

 While machine learning techniques have shown remarkable success in various domains, their application to control has often been hindered by their inherent limitation: a lack of consideration for the underlying physical laws and constraints that govern the behavior of any real-world dynamical system. As a result, the models often lack in trustworthiness and generalizability. However, with the emergence of physics-informed machine learning, a new paradigm is taking shape - one that combines the power of data-driven learning with the foundational principles of physics.

Physics-informed machine learning leverages the inherent knowledge and understanding of the physical world to inform the learning process of machine learning algorithms. By explicitly integrating physical laws, domain expertise, and prior knowledge into the learning framework, physics-informed learning empowers control systems to leverage the flexibility and adaptability of machine learning while remaining grounded in a solid understanding of the underlying dynamics. This fusion enables more efficient and trustworthy learning that results in control applications with superior performance, robustness, and interpretability. The potential applications of physics-informed machine learning for control and optimization are immense and diverse, spanning a wide range of domains. For example, in robotics, physics-informed learning can enhance the control of complex manipulators and autonomous agents by explicitly considering mechanical constraints, kinematics, and dynamics. Furthermore, in power systems and industrial processes, physics-informed learning can optimize control strategies by taking into account physical phenomena, such as heat transfer, fluid dynamics, and thermodynamics.

This workshop aims to provide insight into recent advances in the field of physics-informed machine learning for control and optimization, and sketch some of the open challenges and opportunities using physics-informed machine learning. Experts/lecturers with experience in physics-informed learning and optimization based control will present new results in this area and spotlight challenges and opportunities for the control community as well as recent advances in physics-informed learning in general. 

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.