From 0725c44c61b30ac431f393db5f9cc6a03320fcab Mon Sep 17 00:00:00 2001 From: Dane Sabo Date: Mon, 25 Nov 2024 15:25:57 -0500 Subject: [PATCH] vault backup: 2024-11-25 15:25:57 --- .../plugins/obsidian-style-settings/data.json | 2 +- 4 Qualifying Exam/4 Presentation/Outline.md | 26 +++++++++---------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/.obsidian/plugins/obsidian-style-settings/data.json b/.obsidian/plugins/obsidian-style-settings/data.json index 7ef955c5e..fb008be12 100755 --- a/.obsidian/plugins/obsidian-style-settings/data.json +++ b/.obsidian/plugins/obsidian-style-settings/data.json @@ -4,7 +4,7 @@ "anuppuccin-theme-settings-extended@@anp-theme-ext-light": true, "anuppuccin-theme-settings-extended@@anp-theme-ext-dark": true, "anuppuccin-theme-settings-extended@@catppuccin-theme-extended": "ctp-notion-light", - "anuppuccin-theme-settings-extended@@catppuccin-theme-dark-extended": "ctp-generic-dark", + "anuppuccin-theme-settings-extended@@catppuccin-theme-dark-extended": "ctp-gruvbox-dark", "anuppuccin-theme-settings@@anuppuccin-theme-dark": "ctp-frappe", "anuppuccin-theme-settings@@anp-custom-checkboxes": true, "anuppuccin-theme-settings@@anp-speech-bubble": true, diff --git a/4 Qualifying Exam/4 Presentation/Outline.md b/4 Qualifying Exam/4 Presentation/Outline.md index 20b5e11fa..46c58d1cc 100644 --- a/4 Qualifying Exam/4 Presentation/Outline.md +++ b/4 Qualifying Exam/4 Presentation/Outline.md @@ -14,7 +14,7 @@ Ideas taken from https://services.anu.edu.au/files/development_opportunity/Resea - As a result, we need to reverify robustness on built controllers - This exists for structured perturbations. We # Gap In The Literature -### **Slide 1: Robust Control Foundations** +## **Slide 1: Robust Control Foundations** **Assertion:** Robust control ensures stability despite system discrepancies. **Evidence:** @@ -26,7 +26,7 @@ Ideas taken from https://services.anu.edu.au/files/development_opportunity/Resea --- -### **Slide 2: Structured vs. Unstructured Perturbations** +## **Slide 2: Structured vs. Unstructured Perturbations** **Assertion:** Robust control addresses structured and unstructured perturbations differently. **Evidence:** @@ -38,7 +38,7 @@ Ideas taken from https://services.anu.edu.au/files/development_opportunity/Resea --- -### **Slide 3: Disk-Based Unstructured Uncertainty** +## **Slide 3: Disk-Based Unstructured Uncertainty** **Assertion:** Disk-based perturbation quantifies unstructured uncertainties. **Evidence:** @@ -55,7 +55,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 4: Current Limitations in Robust Control** +## **Slide 4: Current Limitations in Robust Control** **Assertion:** Current methods lack discrete examples of unstructured perturbations. **Evidence:** @@ -66,7 +66,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 5: Diffusion Models as a Solution** +## **Slide 5: Diffusion Models as a Solution** **Assertion:** Diffusion models can generate unstructured perturbations. **Evidence:** @@ -79,7 +79,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 6: Parallels Between Diffusion Models and This Project** +## **Slide 6: Parallels Between Diffusion Models and This Project** **Assertion:** Diffusion models address sparse perturbation generation in engineering. **Evidence:** @@ -90,7 +90,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ # Goals and Outcomes # Research Methodology -### **Slide 1: Research Motivation** +## **Slide 1: Research Motivation** **Assertion:** Current methods for generating unstructured perturbations are limited in flexibility and generalizability. @@ -104,7 +104,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 2: Diffusion Model Features** +## **Slide 2: Diffusion Model Features** **Assertion:** Frequency response data forms the foundation for feature creation in diffusion models. @@ -118,7 +118,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 3: Creating Frequency Features** +## **Slide 3: Creating Frequency Features** **Assertion:** Discretizing the frequency response enables scalable feature sets. @@ -133,7 +133,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 4: Training the Diffusion Model** +## **Slide 4: Training the Diffusion Model** **Assertion:** Diffusion models learn unstructured perturbations through iterative noise transformation. @@ -148,7 +148,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 5: Generating New Perturbations** +## **Slide 5: Generating New Perturbations** **Assertion:** The trained diffusion model generates diverse and flexible perturbations. @@ -163,7 +163,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 6: Ensuring Valid Perturbations** +## **Slide 6: Ensuring Valid Perturbations** **Assertion:** Generated perturbations must meet robust control requirements. @@ -178,7 +178,7 @@ _(Include a visual of how $\Delta$ affects $P$)_ --- -### **Slide 7: Advantages of Diffusion Models** +## **Slide 7: Advantages of Diffusion Models** **Assertion:** Diffusion models provide a novel solution for generating unstructured perturbations.