danesabo/Obsidian
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

vault backup: 2024-10-02 13:37:36

Browse Source
This commit is contained in:
Dane Sabo 2024-10-02 13:37:36 -04:00
parent f5fcd62924
commit a877b897e9
1 changed files with 1 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md
View File

@ -95,4 +95,4 @@ Experimentally verifying robustness for implementations of controllers requires
This research will utilize a diffusion model to make generation of unstructured perturbations easier. The generative diffusion model is great at creating new samples with a controlled amount of distortion compared to training data. We will use this feature of the diffusion model to generate unstructured perturbations. The diffusion model consists of two processes: a forward process that degrades inputs with noise, and a reverse process that the learned model attempts to denoise the input. This reverse process will be trained using time and frequency response data of a variety of dynamic systems, so that noise removal creates samples that look like dynamic systems. Finally, we will control the amount of perturbation by controlling how much we destruct the input of a nominal plant with noise. By introducing less noise we create smaller perturbations, while to create large perturbations we do the opposite. This research will utilize a diffusion model to make generation of unstructured perturbations easier. The generative diffusion model is great at creating new samples with a controlled amount of distortion compared to training data. We will use this feature of the diffusion model to generate unstructured perturbations. The diffusion model consists of two processes: a forward process that degrades inputs with noise, and a reverse process that the learned model attempts to denoise the input. This reverse process will be trained using time and frequency response data of a variety of dynamic systems, so that noise removal creates samples that look like dynamic systems. Finally, we will control the amount of perturbation by controlling how much we destruct the input of a nominal plant with noise. By introducing less noise we create smaller perturbations, while to create large perturbations we do the opposite.
Verifying implementations of controllers has been an onerous task. Perturbations take a significant effort to generate due to the manual nature of current techniques. Generative models have the potential to accelerate this perturbation generation. If successful, having cheaper access to numerous valid perturbations will make robustness verification of control system implementations more accessible. This is a big deal for systems where high assurance is necessary. New infrastructure projects utilize modern digital controllers that suffer from this robustness verification predicament. This research has to potential to reduce the cost of verification of these systems, and in turn, reduce the cost of new infrastructure projects while maximizing system resillience. Verifying implementations of controllers has been an onerous task. Perturbations take a significant effort to generate due to the manual nature of current techniques. Generative models have the potential to accelerate this perturbation generation. If successful, having cheaper access to numerous valid perturbations will make robustness verification of control system implementations more accessible. This is a big deal for systems where high assurance is necessary. New infrastructure projects utilize modern digital controllers that suffer from this robustness verification predicament. This research has to potential to reduce the cost of verification of these systems, and in turn, reduce the cost of new infrastructure projects while maximizing system resilience.