abstract: Image-based process monitoring has recently attracted increasing attention due to the advancement of the sensing technologies. However, existing process monitoring methods fail to fully utilize the spatial information of images due to their complex characteristics including the high dimensionality and complex spatial structures. Recent advancement of the unsupervised deep models such as a generative adversarial network (GAN) and generative adversarial autoencoder (AAE) has enabled to learn the complex spatial structures automatically. Inspired by this advancement, we propose an anomaly detection framework based on the AAE for unsupervised anomaly detection for images. AAE combines the power of GAN with the variational autoencoder, which serves as a nonlinear dimension reduction technique with regularization from the discriminator. Based on this, we propose a monitoring statistic efficiently capturing the change of the image data. The performance of the proposed AAE-based anomaly detection algorithm is validated through a simulation study and real case study for rolling defect detection. / Dissertation/Thesis / Masters Thesis Industrial Engineering 2019

<pre>Operator learning has the potential to supplement traditional numerical methods, especially when speed is desired more than accuracy. <br>This includes the architectures DeepONets, Fourier neural operators and Koopman autoencoders.<br>First, this dissertation provides the background material for operator learning. <br>Then, it studies some general best practices for operator learning.<br>Then, it studies the loss functions and operator forms for Koopman autoencoders. <br>Finally, it studies the use of an adversarial addition to neural operators that have an autoencoder structure.</pre><p></p>