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4 Qualifying Exam/2 Writing/1. QE Goals and Outcomes.md
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title: Goals and Outcomes
allDay: true
date: 2024-09-26
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completed:
type: single
endDate: 2024-10-03
---
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If this research is successful, this diffusion model will accomplish three main tasks:
**Outcome 1:** Approximate a set of controllable plants by generating a large number of perturbed examples. This research will use the lossy nature of the diffusion model to create the perturbation. Inference of these models is relatively cheap, while maintaining the ability to create novel samples.
**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
**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.