From da0ac421d99ab6107c1c07c68b7e0187606fc5f7 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Mon, 30 Sep 2024 21:36:27 -0400 Subject: [PATCH] vault backup: 2024-09-30 21:36:27 --- 4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) 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 ed083a87f..13141b398 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 @@ -89,4 +89,4 @@ If this research is successful, this diffusion model will accomplish three main **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. \ No newline at end of file +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. This set of plants encompasses a 'distance' from a nominal plant that \ No newline at end of file