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171980 https://hdl.handle.net/2286/R.2.N.171980 http://rightsstatements.org/vocab/InC/1.0/ All Rights Reserved 2022 47 pages Masters Thesis Academic theses Text eng Shi, Wenlong Ren, Yi Hong, Qijun Jiao, Yang Yang, Yezhou Arizona State University Partial requirement for: M.S., Arizona State University, 2022 Field of study: Mechanical Engineering The increasing availability of data and advances in computation have spurred the development of data-driven approaches for modeling complex dynamical systems. These approaches are based on the idea that the underlying structure of a complex system can be discovered from data using mathematical and computational techniques. They also show promise for addressing the challenges of modeling high-dimensional, nonlinear systems with limited data. In this research expository, the state of the art in data-driven approaches for modeling complex dynamical systems is surveyed in a systemic way. First the general formulation of data-driven modeling of dynamical systems is discussed. Then several representative methods in feature engineering and system identification/prediction are reviewed, including recent advances and key challenges. Artificial Intelligence Robotics Data-driven Methods for Modeling Complex Dynamical System