danesabo/Obsidian
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

vault backup: 2024-10-31 13:57:24

Browse Source
This commit is contained in:
Dane Sabo 2024-10-31 13:57:24 -04:00
parent 852dd503bb
commit e3aaed9024
3 changed files with 19 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

14
.obsidian/bookmarks.json vendored Normal file
View File

@ -0,0 +1,14 @@
{
"items": [
{
"type": "file",
"ctime": 1730397400319,
"path": "0 Managerial Pages/To Do List.md"
},
{
"type": "file",
"ctime": 1730397409494,
"path": "4 Qualifying Exam/1 Managing Stuff/0. QE To Do List.md"
}
]
}

2
2 Cole Group Meeting Notes/Weekly Note 2024-10-30.md
View File

@ -42,7 +42,7 @@ where readstatus and dateread => this.date-dur(7)
# This Week's Plan
Ass in chair. Don't stop writing.
Halloween event.
Figure out what the fuck the Lobot model is. (Constraint Refinement Galois HARDENs report)
Figure out what the fuck the Lobot model is. (Constraint Refinement Galois HARDENs report). Come back with something that sort of talks about what this thing does. Converse with people above and below in the stack about how we connect.
## Tasks This Next Week
### Tasks Overdue
```dataview

11
4 Qualifying Exam/2 Writing/3. QE Research Approach.md
View File

@ -78,11 +78,8 @@ Restate the main goal: to create a diffusion model that can generate unstructure
**4.3 Diffusion Model Reverse Process for Perturbation Generation**: Explain the reverse process, where a neural network denoises the plant sample to produce novel, unstructured perturbations.
- **4.4 Experimental Design for Verifying Perturbation Validity**: Outline how you’ll validate that the generated plants belong to the allowable set, ensuring they meet robust stability/performance criteria by checking Nyquist stability.
### **Expected Outcomes and Verification of Results**
#### ***5.1 Outcome 1**: A set of valid unstructured perturbations that satisfy robustness requirements.
##### testing testing!
###### aaaaaaaaaaaaaaaaaaaa
**5.2 Outcome 2**: Quantitative assessment of the model’s ability to approximate the uncertainty bounds in W2W_2W2​.
**5.3 Outcome 3**: Reduced effort in generating perturbations for robustness verification in controller implementation.
***5.1 Outcome 1**: A set of valid unstructured perturbations that satisfy robustness requirements.
**5.2 Outcome 2**: Quantitative assessment of the model’s ability to approximate the uncertainty bounds in W2W_2W2​.
**5.3 Outcome 3**: Reduced effort in generating perturbations for robustness verification in controller implementation.
### **Implications and Potential Applications**
- Discuss broader implications for robust control, such as reducing the cost and complexity of verification processes for infrastructure systems. Explain how success could advance diffusion models’ applications beyond robust control, perhaps influencing fields requiring resilient system validation.
Discuss broader implications for robust control, such as reducing the cost and complexity of verification processes for infrastructure systems. Explain how success could advance diffusion models’ applications beyond robust control, perhaps influencing fields requiring resilient system validation.