From 25e296e862f438607aae1f6e83d6da1b9590c140 Mon Sep 17 00:00:00 2001
From: Dane Sabo <dane.sabo@pitt.edu>
Date: Mon, 25 Nov 2024 15:36:11 -0500
Subject: [PATCH] vault backup: 2024-11-25 15:36:11

---
 4 Qualifying Exam/4 Presentation/Outline.md | 140 +++++++++++++++++++-
 1 file changed, 139 insertions(+), 1 deletion(-)

diff --git a/4 Qualifying Exam/4 Presentation/Outline.md b/4 Qualifying Exam/4 Presentation/Outline.md
index 96b25c7f..ee3f8b18 100644
--- a/4 Qualifying Exam/4 Presentation/Outline.md	
+++ b/4 Qualifying Exam/4 Presentation/Outline.md	
@@ -265,6 +265,144 @@ _(Include a visual of how $\Delta$ affects $P$)_
 - Example journal or conference targets.
 - Overview of the dissemination process.
 # Metrics of Success
+## **Slide 1: Metrics of Success Overview**
 
+**Assertion:** Project success will be evaluated through milestone tracking and outcome-based metrics.
+
+- **Evidence:**
+    1. Goals and Outcomes: Milestones tied to the objectives of this research.
+    2. Unstructured Perturbation Evaluation: Metrics to assess diffusion model output.
+
+**Visuals:**
+
+- High-level flowchart showing the two categories of success metrics.
+
+---
+
+## **Slide 2: Goals and Outcomes**
+
+**Assertion:** The research aims to deliver specific capabilities for creating unstructured perturbations.
+
+- **Evidence:**
+    - Approximate unstructured sets through numerous perturbed plants.
+    - Perturb nominal plants using the diffusion model.
+    - Generate frequency-domain responses from training data.
+
+**Visuals:**
+
+- Table summarizing the three goals and their significance.
+- Conceptual graphic of a nominal plant with perturbed versions around it.
+
+---
+
+## **Slide 3: Unstructured Perturbation Evaluation**
+
+**Assertion:** The diffusion model's success will be judged on distribution and diversity of perturbations.
+
+- **Evidence:**
+    - Distribution: Verify uniform coverage of the multiplicative uncertainty disk.
+    - Diversity: Assess non-parametric, dissimilar perturbations among examples.
+
+**Visuals:**
+
+- Example complex plane with plotted perturbed plants.
+- Graph comparing similarity metrics across perturbations.
+
+---
+
+## **Slide 4: Statistical Evaluation (Optional Deep Dive)**
+
+**Assertion:** Statistical analysis ensures robustness and diversity in generated perturbations.
+
+- **Evidence:**
+    - Standard statistical tests applied to the perturbation set.
+    - Covariance vectors calculated for key frequency ranges.
+
+**Visuals:**
+
+- Example statistical output or covariance plot for one frequency band.
+- Caption explaining its role in validating uniform coverage.
 # Risks and Contingencies
-# 
\ No newline at end of file
+## **Slide 1: Risks and Contingencies Overview**
+
+**Assertion:** This research has identified key risks and developed contingencies to address them.
+
+- **Evidence:**
+    1. Computational demands of diffusion models.
+    2. Training data sufficiency.
+    3. Generalization of interpolation methods to perturbations.
+
+**Visuals:**
+
+- A risk-contingency matrix outlining the key challenges and corresponding mitigations.
+
+---
+
+## **Slide 2: Risk 1 - Computational Demands**
+
+**Assertion:** Diffusion models may require significant computational resources during training and inference.
+
+- **Evidence:**
+    - Reverse process inference is computationally intensive due to per-step calculations.
+    - Training complexity scales with model structure and feature count.
+
+**Contingencies:**
+
+1. Utilize the University of Pittsburgh’s CRC supercomputing resources.
+2. Reduce data features while monitoring model performance.
+
+**Visuals:**
+
+- Diagram comparing computational cost across time steps.
+- Icon of computational resources with CRC logo or similar.
+
+---
+
+## **Slide 3: Risk 2 - Insufficient Training Data**
+
+**Assertion:** Structured perturbations alone may not condition the model adequately.
+
+- **Evidence:**
+    - Structured perturbations simplify training but may lack diversity.
+
+**Contingencies:**
+
+1. Augment training with manually or algorithmically generated $\Delta$ examples (e.g., bounded by supermum gain $\beta$).
+2. Diversify training data sources to improve robustness.
+
+**Visuals:**
+
+- Example of structured vs. manual perturbation samples on the complex plane.
+- Flowchart showing training data augmentation process.
+
+---
+
+## **Slide 4: Risk 3 - Interpolation Limitations**
+
+**Assertion:** Interpolation methods may fail to regulate perturbations effectively.
+
+- **Evidence:**
+    - Image-based interpolation success may not generalize to this domain.
+
+**Contingencies:**
+
+1. Implement $r(\mathcal{P}_t)$-based reverse process steering for controlled perturbations【cite sources】.
+2. Explore alternative interpolation techniques tailored to frequency domain applications.
+
+**Visuals:**
+
+- Conceptual illustration of $r(\mathcal{P}_t)$ steering function in reverse process.
+- Example showing failure of simple interpolation and correction with $r(\mathcal{P}_t)$.
+
+---
+
+## Slide 5: Risk Mitigation Framework (Optional Summary Slide)
+
+**Assertion:** Addressing risks proactively ensures project success.
+
+- **Evidence:**
+    - Computational strategies, diversified training, and alternative steering methods safeguard outcomes.
+
+**Visuals:**
+
+- Funnel graphic showing risks addressed through mitigations leading to project success.
\ No newline at end of file