Presenter: Yuanyuan Shi

Description: In this talk, I will share our recent progress on developing learning algorithms for real-world energy system control, with stability and computational tractability guarantees. The first application is power grid voltage control. I will introduce a novel neural network architecture – monotone neural network (MNN) that ensure the network output is a monotone function of the input. MNN is achieved by first designing neural networks that are convex (with universal approximation guarantee) and using gradients of convex functions to ensure monotonicity. We show that MNN is a powerful structure for voltage control – with stability guarantee and superior performance compared to standard neural networks. The second application is building control. There is an emergent need to model indoor air quality to improve occupant health and building energy efficiency. A fundamental challenge is that building airflow dynamics are governed by nonlinear partial differential equations (PDEs) with unknown parameters, which are computationally prohibitive from a real‑time control perspective. I will introduce our work on PDE‑constrained optimization for building model identification and designing neural operator learning for efficient PDE system control.
 

Bio: Yuanyuan Shi is an Assistant Professor at the Department of Electrical and Computer Engineering at the University of California San Diego. She received her Ph.D. in Electrical and Computer Engineering (ECE), masters in ECE and Statistics, all from the University of Washington, in 2020. From 2020 to 2021, she was a Postdoctoral Scholar at Caltech. Her research focuses on machine learning, dynamical systems and control, with applications to sustainable energy systems. She is a recipient of multiple awards, including the Hellman Fellowship in 2023 and the UW Scientific Achievement Award for her thesis work in 2020.
 

Hosted by: Hao Ye / ECE