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+\title{QE Abstract For Dan}
+\author{}
+\date{}
+
+\begin{document}
+\maketitle
+
+\textbf{Diffusion Generative Models For Unstructured Uncertainty
+Perturbations}
+
+Real world control systems operate on physical plants that can have
+different dynamics than a nominal model. This discrepancy is called a
+perturbation, and can affect controller performance. The field of robust
+control creates a way to establish a set of allowable perturbations for
+a given plant, controller, and design requirements. We can make
+guarantees that a controller meets performance or safety criterion when
+the real plant does not perfectly match the nominal model.
+
+A model controller can be proven to control a set of plants, but a real
+controller can only control one plant at a time. Validating robustness
+for a real controller requires extracted elements of the perturbed set,
+which can be deceptively difficult to create. Perturbed plants are
+commonly generated by using a structured uncertainty, where an engineer
+creates distributed ranges for system parameters. These distributions
+are then sampled and used to create a perturbed plant. This is an
+knowledge intensive and time consuming process.
+
+We suggest using generative artificial intelligence to efficiently
+create perturbed plants. The diffusion generative model has shown great
+promise in creating novel and realistic samples from training data. This
+model can be used to remove the laborious effort of creating perturbed
+plants. We suggest training a generative model to create Bode plots of
+transfer functions. This trained model will then be given a warm start
+with the nominal plant as an input, with which it will then be able to
+generate a limitless number of unique perturbed plants for controller
+validation.
+
+\textbf{STATS: 249 / 250 words}
+
+\end{document}