Merging First-Principles with Machine Learning for the Optimization of Process and Energy Systems
Author(s)
Kilwein, Zachary Alexander
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Abstract
The work provided in this doctoral dissertation describes how state-of-the-art computational models built upon first-principle scientific and engineering knowledge interface and combine with data-driven methodologies. Due to the large variability within each of the two major modeling domains, the work presented spans many disciplines, data-sources, and applications to find symbiotic ways to merge both of these tools. The underlying threads throughout this dissertation include the use of hybrid modeling methods to increase the speed or accuracy of challenging optimization problems, the use of novel machine-learning architectures for problems that require components with fast on-line prediction, a focus on applications relevant to security and resilience with respect to energy infrastructure, and details on the dependability and verifiability of machine learning model components.
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Date
2024-04-28
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Text
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Dissertation