2026-09-18T20:51:22Zhttps://ecommons.cornell.edu/server/oai/requestoai:ecommons.cornell.edu:1813/1031202026-05-15T19:42:53Zcom_1813_35col_1813_47
Jing, Dongheng
Ferrari, Silvia
Petersen, Kirstin Hagelskjaer
2021-03-12T17:43:39Z
2021-03-12T17:43:39Z
2020-08
ProQuest Submission ID: 11058
ProQuest Publication ID: 28089177
https://hdl.handle.net/1813/103120
https://doi.org/10.7298/52gm-yn11
13277684
67 pages
Knowing the equation of an unknown dynamical system is essential when trying to apply optimal control. Sometimes researchers do not have a comprehensive knowledge to a nonlinear system. The unknown part might be the function representing the relation between states (e.g. transfer function), or key parameters of a dynamical system (e.g. proportional constant of spring in a linear spring system). Various methods have been developed to identify the dynamics of an unknown system. In this thesis, multiple approaches include Neural Network polynomial Extraction (NN-poly), Sparse Identification of nonlinear Dynamics (SINDy) and Non-Uniform Discrete Fourier Transform (NUDFT) are compared over their ability to find the expression of unknown systems or to estimate key parameters of a dynamical system. Multiple tasks with different purposes are created to test the performances of these methods.
en
METHODS COMPARISON ON FLOW MODEL CONSTRUCTION AND PARAMETER ESTIMATION
dissertation or thesis
https://catalog.library.cornell.edu/catalog/13277684
application/pdf
Mechanical Engineering
Cornell University
Master of Science
M.S., Mechanical Engineering
https://hdl.handle.net/1813/59810
Publication
Cornell Theses and Dissertations
Jing, Dongheng
5893a6ea-7af3-41d7-abc6-04bcd26ab5df
open.access
open.access
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<Abstract>Knowing the equation of an unknown dynamical system is essential when trying to apply optimal control. Sometimes researchers do not have a comprehensive knowledge to a nonlinear system. The unknown part might be the function representing the relation between states (e.g. transfer function), or key parameters of a dynamical system (e.g. proportional constant of spring in a linear spring system). Various methods have been developed to identify the dynamics of an unknown system. In this thesis, multiple approaches include Neural Network polynomial Extraction (NN-poly), Sparse Identification of nonlinear Dynamics (SINDy) and Non-Uniform Discrete Fourier Transform (NUDFT) are compared over their ability to find the expression of unknown systems or to estimate key parameters of a dynamical system. Multiple tasks with different purposes are created to test the performances of these methods.</Abstract>
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