Seminar: A neural network approach for high-dimensional real-time optimal control

Su Mo Tu We Th Fr Sa
29 30 31 1 2 3 4
5 6 7 8 9 10 11
12 13 14 15 16 17 18
19 20 21 22 23 24 25
26 27 28 29 30 1 2
Date/Time:Friday, 01 Oct 2021 from 4:00 pm to 5:00 pm
Contact:Hailiang Liu
Actions:Download iCal/vCal | Email Reminder
Join this seminar to hear about how this research group is approaching training neural networks to synthesize real-time controls.

Join this webinar to hear from Dr. Levon Nurbepkyan from the Department of Mathematics at UCLA.

Abstract: Due to fast calculation at the deployment, neural networks (NN) are attractive for real-time applications. I will present one possible approach for training NN to synthesize real-time controls. A key aspect of our method is the combination of the following features: 1. No data generation and fitting 2. Direct optimization of trajectories 3. The correct structural ansatz for the approximations of optimal control With these techniques, we can solve problems in hundreds of dimensions. We also find some unexpected generalization properties. The talk is based on two recent papers and Brief bio: I am currently an Assistant Adjunct Professor at the Department of Mathematics at UCLA. I obtained my Ph.D. in the framework of UT Austin -- Portugal CoLab under the supervision of Professors Diogo Gomes and Alessio Figalli. My research interests include calculus of variations, optimal control theory, mean-field games, partial differential equations, mathematics applied to machine learning, dynamical systems, and shape optimization problems. I have been a Senior Fellow at the Institute for Pure and Applied Mathematics (IPAM) at UCLA for its Spring 2020 Program on High Dimensional Hamilton-Jacobi PDEs and a Simons CRM Scholar at the Centre de Recherches Mathematiques (CRM) at the University of Montreal for its Spring 2019 Program on Data Assimilation: Theory, Algorithms, and Applications. You can also find info at