# Table of Contents for 2 Writing

## Files
- [[0. QE Abstract.md]]
- [[1. QE Goals and Outcomes.md]]
- [[2. QE State of the Art.md]]
- [[3. QE Research Approach.md]]
- [[4. QE Broader Impacts.md]]
- [[5. QE Metrics of Success.md]]
- [[6. QE Risks and Contingencies.md]]
- [[7. QE One Pager.md]]
- [[8. QE Oral Exam Presentation.md]]
- [[QE Abstract For Dan.md]]
- [[QE Abstract For Dan.tex]]
- [[test.bib]]
- [[Untitled.bib]]
- [[Untitled.tex]]

## Summary
Based on the outline and draft content, I'll provide a summary of the key points and suggestions for improvement.

**Summary**

The proposal aims to use a generative diffusion model to create unstructured perturbations for robustness verification in controller implementation. The research goals include training a diffusion model with structured perturbations, introducing noise through the forward process, and generating novel perturbations through the reverse process. The expected outcomes include:

1. A set of valid unstructured perturbations that satisfy robust stability/performance criteria.
2. Quantitative assessment of the model's ability to approximate uncertainty bounds in W2W_2W2​.
3. Reduced effort in generating perturbations for robustness verification.

**Suggestions**

1. **Clearly define the scope and limitations**: Specify the context, constraints, and assumptions of the proposal to ensure a clear understanding of the research goals and objectives.
2. **Provide more details on the uncertainty function W2W_2W2​**: Explain why this function is relevant to robustness verification and how its parameters will be chosen.
3. **Outline the experimental design for validating perturbation validity**: Describe the steps involved in verifying that generated plants belong to the allowable set, ensuring they meet robust stability/performance criteria by checking Nyquist stability.
4. **Quantify the expected outcomes**: Use specific metrics or indicators to measure the success of the proposal, such as the percentage of valid perturbations or the reduction in effort required for robustness verification.
5. **Consider additional evaluation criteria**: Think about other factors that could be used to evaluate the proposal's success, such as computational efficiency, model interpretability, or applicability to different types of systems.

**Additional Ideas**

1. **Explore potential applications beyond robust control**: Discuss how this research could contribute to other fields requiring resilient system validation, such as infrastructure systems, healthcare, or finance.
2. **Discuss potential challenges and risks**: Acknowledge potential challenges, such as the difficulty in defining valid perturbations or the risk of over- or under-representing uncertainty bounds.

By addressing these suggestions and considering additional ideas, you can strengthen your proposal and increase its chances of success.

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