Date of Award:
8-2022
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Mathematics and Statistics
Committee Chair(s)
Jia Zhao
Committee
Jia Zhao
Committee
Zhaohu Nie
Committee
Joe Koebbe
Abstract
This thesis presents a novel method, recursive Physics informed neural network, to learn the right hand side of differential equations. The neural network takes in data, then trains, and then acts as a proxy for the differential equation which can be used for modeling. We show the theoretical superiority of the recursive approach. We also use computer simulations to demonstrate the proved properties.
Checksum
ffb5ee6bfa1fbd5304891be83e50cabb
Recommended Citation
Mau, Jarrod, "Dynamic System Discovery with Recursive Physics-Informed Neural Networks" (2022). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 8524.
https://digitalcommons.usu.edu/etd/8524
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