diff --git a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md
index be2f9265..ed083a87 100644
--- a/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md	
+++ b/4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md	
@@ -87,4 +87,6 @@ If this research is successful, this diffusion model will accomplish three main
 
 **Outcomes 2:** Perturb a nominal plant in an unstructured manner with a controllable amount of uncertainty. The diffusion model uses Gaussian noise as a mechanic to introduce perturbation from training data. This noise is not predicated on any understanding of the physical properties of a system, but instead is a mathematical process. For this reason, we call the perturbation 'unstructured'. The amount of noise can also be tuned: if less perturbation is desired, less noise is introduced. If greater perturbation is required, more noise is introduced.
 
-**Outcome 3:** Generate time and frequency domain responses based on training data of example systems. The diffusion model is like any other machine learning model: it requires training data. For this diffusion model, we will create training data of physically realizable plants. 
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+**Outcome 3:** Generate time and frequency domain responses based on training data of example systems. The diffusion model is like any other machine learning model: it requires training data. For this diffusion model, we will create training data of physically realizable plants dynamics. This training data will teach the diffusion model to create realistic time and frequency responses as novel samples. 
+
+Perturbing a nominal plant to establish robustness is not a new technique. For a specified controller, robust control can find the set of plants with which the controller remains performant. 
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