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Dane Sabo 2024-11-25 15:25:57 -05:00
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2
.obsidian/plugins/obsidian-style-settings/data.json vendored
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@ -4,7 +4,7 @@
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"anuppuccin-theme-settings-extended@@catppuccin-theme-extended": "ctp-notion-light", "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@@anuppuccin-theme-dark": "ctp-frappe",
"anuppuccin-theme-settings@@anp-custom-checkboxes": true, "anuppuccin-theme-settings@@anp-custom-checkboxes": true,
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4 Qualifying Exam/4 Presentation/Outline.md
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@ -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 - As a result, we need to reverify robustness on built controllers
- This exists for structured perturbations. We - This exists for structured perturbations. We
# Gap In The Literature # Gap In The Literature
### **Slide 1: Robust Control Foundations** ## **Slide 1: Robust Control Foundations**
**Assertion:** Robust control ensures stability despite system discrepancies. **Assertion:** Robust control ensures stability despite system discrepancies.
**Evidence:** **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. **Assertion:** Robust control addresses structured and unstructured perturbations differently.
**Evidence:** **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. **Assertion:** Disk-based perturbation quantifies unstructured uncertainties.
**Evidence:** **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. **Assertion:** Current methods lack discrete examples of unstructured perturbations.
**Evidence:** **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. **Assertion:** Diffusion models can generate unstructured perturbations.
**Evidence:** **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. **Assertion:** Diffusion models address sparse perturbation generation in engineering.
**Evidence:** **Evidence:**
@ -90,7 +90,7 @@ _(Include a visual of how $\Delta$ affects $P$)_
# Goals and Outcomes # Goals and Outcomes
# Research Methodology # Research Methodology
### **Slide 1: Research Motivation** ## **Slide 1: Research Motivation**
**Assertion:** Current methods for generating unstructured perturbations are limited in flexibility and generalizability. **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. **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. **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. **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. **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. **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. **Assertion:** Diffusion models provide a novel solution for generating unstructured perturbations.