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dc.contributor.advisorBiedron, Sandra
dc.contributor.advisorMilton, Stephen
dc.contributor.authorEdelen, Auralee Linscott
dc.contributor.committeememberChong, Edwin
dc.contributor.committeememberJohnson, Thomas
dc.descriptionIncludes bibliographical references.
dc.description2020 Summer.
dc.description.abstractCharged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020- CSU Theses and Dissertations
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dc.rightsCopyright of original work is retained by the authors.
dc.rights.licenseThis article is open access and distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).
dc.rights.licenseThis work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 United States License.
dc.subjectmodel predictive control
dc.subjectparticle accelerators
dc.subjectneural networks
dc.subjectmachine learning
dc.titleNeural networks for modeling and control of particle accelerators
dcterms.rights.dplaThe copyright and related rights status of this Item has not been evaluated ( Please refer to the organization that has made the Item available for more information. and Computer Engineering State University of Philosophy (Ph.D.)

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