Mathematical and Statistical Modeling with Deep Neural Networks

Author

Albert Romero, The University of Texas Rio Grande Valley

Date of Award

5-2022

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Applied Statistics and Data Science

First Advisor

Dr. Hansapani Rodrigo

Second Advisor

Dr. Tamer F. Oraby

Third Advisor

Dr. Beiyu Lin

Abstract

General adversarial networks (GANs) are a form of machine learning that includes two neural networks competing in a zero-sum game. One network produces artificial, while the other tries to distinguish artificial data from real. The Wasserstein general adversarial network with gradient penalty (WGAN-GP) variant of this technique is used to produce solutions for ordinary and partial differential equations.

Comments

Copyright 2022 Albert Romero. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/mathematical-statistical-modeling-with-deep/docview/2699728242/se-2?accountid=7119

Recommended Citation

Romero, Albert, "Mathematical and Statistical Modeling with Deep Neural Networks" (2022). Theses and Dissertations. 1095.
https://scholarworks.utrgv.edu/etd/1095