Data-driven Methods for Modeling Complex Dynamical System
Description
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.
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2022
Agent
- Author (aut): Shi, Wenlong
- Thesis advisor (ths): Ren, Yi
- Committee member: Hong, Qijun
- Committee member: Jiao, Yang
- Committee member: Yang, Yezhou
- Publisher (pbl): Arizona State University