Wednesday, April 23
SEC LL2.221
11:00am

Physics-Informed Learning and Control for Intelligent Transportation: Theory, Algorithms, and Experimental Validations
Zhe Fu, Ph.D. Candidate, University of California Berkeley

Abstract: The rapid growth of data science is reshaping how we model and control physical infrastructure systems. Traditional PDE-based methods provide structured interpretability, whereas purely data-driven neural networks offer flexibility but often lack adherence to physical principles. In this talk, I will present a physics-informed learning and control framework that combines PDE-based modeling with neural networks, enabling improved understanding and predictive accuracy of transportation system dynamics. Specifically, I will introduce a Neural Finite Volume Method (NFVM) that preserves crucial physical properties, effectively bridging physics and data-driven approaches. Motivated by the potential of leveraging this improved understanding to influence real transportation systems, I developed control strategies using a small number of "leader" vehicles to guide traffic flow toward greater efficiency and lower energy consumption, with minimal system-wide intervention. These approaches include a kernel-based control method and an imitation learning strategy, with variations validated in a large-scale operational field experiment involving 100 autonomous vehicles. I will conclude by highlighting ongoing comparative studies to quantify how incorporating physics-informed modeling further enhances control performance in terms of efficiency, safety, and robustness.

Bio: Zhe Fu is a Ph.D. candidate in Transportation Engineering and an M.S. candidate in Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley. Her research lies at the intersection of transportation systems, control theory, and machine learning, with the goal of enabling intelligent and energy-efficient mobility in mixed autonomy environments. She has been recognized as a 2025 Eno Fellow and has received several national honors, including the Rising Stars in NSF CPS Award (2025), Rising Stars in Mechanical Engineering Award (2024), First Place Winner in the INFORMS Best Poster Competition (2023) and Second Place Winner in Berkeley Grad Slam (2025). Her leadership, mentorship, and teaching efforts have been recognized by UC Berkeley and external organizations such as ITS/CTF, EDGE in Tech, H2H8 and AAa/e.

Host: Professor Heng Yang