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Ethan N. Evans
(Advisor: Evangelos A. Theodorou)
will propose a doctoral thesis entitled,
Infinite Dimensional Optimization for Optimal Control and Co-design of Stochastic Spatio-Temporal Processes
On
Wednesday, December 2 at 3:00 p.m.
on BlueJeans: https://bluejeans.com/249893730
Abstract
There is a rising interest in Spatio-temporal systems described by Partial Differential Equations (PDEs) among the control, robotics, and machine learning communities. Related methods usually treat these infinite dimensional problems in finite dimensions with reduced order models. This leads to committing to specific approximation schemes and the subsequent control laws cannot generalize outside of the approximation schemes. Additionally, related work does not consider spatio-temporal descriptions of noise that realistically represent the stochastic nature of physical systems. The prior work section of this proposal provides several open-loop, MPC, and explicit closed loop algorithms for control of semilinear SPDEs, based on generalizations of variational optimization for SPDEs evolving on time-indexed Hilbert spaces. It also provides a derivation of differential dynamic programming for nonlinear PDEs. I propose several extensions to the prior work, including a sampling-based feedback control architecture for open quantum systems with non-demolition measurement, an application of prior work to realistic models of soft-body robotic manipulators, a Deep Forward-Backward Stochastic Differential Equation control scheme for stochastic spatio-temporal systems, and a tensor-train decomposition approach to attain scalability in the DDP of Fields framework.
Committee
