Anderson, Loren2022-08-292022-08-292022-04https://hdl.handle.net/11299/241402University of Minnesota Ph.D. dissertation. 2022. Major: Mathematics. Advisor: Fadil Santosa. 1 computer file (PDF); 113 pages.We perform a comprehensive study on Bayesian sequential optimal experimental design techniquesapplied to inverse problems. We transform the Bayesian sequential optimal experimental design problem into a reinforcement learning problem to gauge the power of recent deep reinforcement learning algorithms compared to other baseline algorithms. Using KL-divergence as a measure of information gain, we construct objectives to maximize information gain for batch design, greedy design, black-box Bayesian optimization, multi-armed bandit optimization, dynamic programming, approximate dynamic programming, and reinforcement learning. This work showcases novel comparisons between the aforementioned methods and a new application of off-the-shelf reinforcement learning algorithms to Bayesian sequential optimal experimental design for inverse problems in differential equation models.enBayesian Experimental DesignDeep Reinforcement LearningInformation GainInverse ProblemsOptimal Experimental DesignSequential Experimental DesignThesis or Dissertation