Date of Award
Master of Science (MS)
Applied Statistics and Data Science
Dr. Hansapani Rodrigo
Dr. Tamer F. Oraby
Dr. Beiyu Lin
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.
Romero, Albert, "Mathematical and Statistical Modeling with Deep Neural Networks" (2022). Theses and Dissertations - UTRGV. 1095.