You're invited to attend
(Advisor: Evangelos Theodorou)
"Stochastic Optimization for Dynamical Systems"
Tuesday, January 11
8:00 AM EST
Montgomery Knight Building, MK 317
Sampling-based dynamic optimization methods have seen many different applications in recent years. Algorithms of this type relies on a control policy distribution to generate control trajectory realizations, which are used to propagate the system states. The cost of each state-control trajectory is then evaluated and used to update the control policy distribution. This proposal presents three different perspectives for deriving sampling-based dynamic optimizers, namely stochastic search, variational inference and variational optimization. Each perspective provides its unique benefits in algorithmic development. Three different future research directions are proposed to improve the applicability, performance, and robustness of the framework. On the applicability side, future research involves application of sampling-based algorithms to quantum systems and social models described by Hawkes processes. On the performance and robustness side, the proposed research is on incorporating the robust covariance steering framework and fractional Gaussian noise for improved exploration.
- Evangelos Theodorou (Advisor), Aerospace Engineering, Georgia Institute of Technology
- Arkadi Nemirovski, Industrial and Systems Engineering, Georgia institute of Technology
- Enlu Zhou, Industrial and Systems Engineering, Georgia institute of Technology