Date of Award
2025
Document Type
Thesis
Degree Name
Master of Science in Engineering (MSE)
Department
Electrical and Computer Engineering
Committee Chair
Aubrey Beal
Committee Member
Laurie Joiner
Committee Member
Ned Corron
Research Advisor
Aubrey Beal
Subject(s)
Data structures (Computer science), Chaotic behavior in systems, Nonlinear theories, Machine learning
Abstract
The research areas of dynamics and data-driven modeling overlap in existing literature. Some research uses dynamical systems as a test bed while developing new modeling methods. Other papers attempt to use data-driven modeling as a tool to better understand, or potentially predict the behavior of a dynamical system. The primary goal of this thesis is to further the symbiotic relationship between these two research areas. Firstly, this paper provides a concrete example of how a basis function decomposition of the Lorenz system can provide a quantitative performance metric for data-driven modeling methods. Secondly, a novel application of an existing modeling method will yield an unexpected Lorenz-Like equation. Finally, this paper discusses preliminary work toward a novel application of data-driven modeling in the field of optics, a research area that remains at the edge of human knowledge. By exploring these three topics, which exist in the intersection of dynamics and data- driven modeling, this thesis aims to provide tools, insights, and intuition that will aid collaborative research of dynamics and data-driven modeling.
Recommended Citation
Tseng, Mattan, "Nonlinear dynamics and data driven modeling" (2025). Theses. 745.
https://louis.uah.edu/uah-theses/745
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