Date of Award:
Master of Science (MS)
Mathematics and Statistics
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.
Mau, Jarrod, "Dynamic System Discovery with Recursive Physics-Informed Neural Networks" (2022). All Graduate Theses and Dissertations. 8524.
Copyright for this work is retained by the student. If you have any questions regarding the inclusion of this work in the Digital Commons, please email us at .