{"id":{"repo_id":"umn","oai_identifier":"oai:conservancy.umn.edu:11299/241402"},"canonical_url":"https://search.dev.ndltd.org/etd/umn/oai:conservancy.umn.edu:11299/241402","repository":{"repo_id":"umn","name":"University of Minnesota","base_url":"https://conservancy.umn.edu/server/oai/request"},"display":{"title":"BAYESIAN SEQUENTIAL OPTIMAL EXPERIMENTAL DESIGN FOR INVERSE PROBLEMS USING DEEP REINFORCEMENT LEARNING","abstract":"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.","abstract_html":"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. 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Major: Mathematics. Advisor: Fadil Santosa. 1 computer file (PDF); 113 pages."]},{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["BAYESIAN SEQUENTIAL OPTIMAL EXPERIMENTAL DESIGN FOR INVERSE PROBLEMS USING DEEP REINFORCEMENT LEARNING"]}]}],"canonical_facts":{"dc:creator":["Anderson, Loren"],"dc:date.accessioned":["2022-08-29T19:24:19Z"],"dc:date.available":["2022-08-29T19:24:19Z"],"dc:date.issued":["2022-04"],"dc:description":["University of Minnesota Ph.D. dissertation. 2022. Major: Mathematics. Advisor: Fadil Santosa. 1 computer file (PDF); 113 pages."],"dc:description.abstract":["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."],"dc:identifier.uri":["https://hdl.handle.net/11299/241402"],"dc:language.iso":["en"],"dc:subject":["Bayesian Experimental Design","Deep Reinforcement Learning","Information Gain","Inverse Problems","Optimal Experimental Design","Sequential Experimental Design"],"dc:title":["BAYESIAN SEQUENTIAL OPTIMAL EXPERIMENTAL DESIGN FOR INVERSE PROBLEMS USING DEEP REINFORCEMENT LEARNING"],"dc:type":["Thesis or Dissertation"]},"updated_at":"2026-07-24T05:19:52Z"}