From 9d629182628b0464cd7deb84d072c51012094c99 Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Wed, 2 Oct 2024 10:24:03 -0400 Subject: [PATCH] vault backup: 2024-10-02 10:24:03 --- 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 05392c72..4aeef158 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. Robust control can find the set of plants with which a controller remains performant. This set of plants encompasses a 'distance' from a nominal plant that \ No newline at end of file +Perturbing a nominal plant to establish robustness is not a new technique. Robust control can find the set of plants with which a controller remains performant. Finding this set is a well understood problem, and can be straightforward. An engineer can use this set of plants to say how robust a nominal controller is to perturbation, and make guarantees about \ No newline at end of file