vault backup: 2024-08-29 17:30:51

.obsidian/workspace.json

@@ -27,8 +27,7 @@
               }
             }
           }
-        ],
-        "currentTab": 1
+        ]
       }
     ],
     "direction": "vertical"
@@ -178,7 +177,7 @@
       "obsidian-comments:Show Comments Panel": false
     }
   },
-  "active": "3c8fa3261c26a8f5",
+  "active": "b5b85c36ff87e102",
   "lastOpenFiles": [
     "3-99 Research/6. Researching Techniques/Highlighting Colors and What they Mean.md",
     "4. Qualifying Exam/2. Writing/QE Abstract.md",

1. Daily Notes/8. August/2024-08-29.md

@@ -24,7 +24,7 @@ tags:
 - [ ] 
 
 # Calendar Tasks
--  QE Abstract [startTime:: 15:00]  [endTime:: 17:00]
+-  QE Abstract [startTime:: 15:00]  [endTime:: 17:45]
 -  August Finances [startTime:: 09:00]  [endTime:: 10:30]
 -  Lunch [startTime:: 12:30]  [endTime:: 14:30]
 -  Seminar travel [startTime:: 10:30]  [endTime:: 12:30]

4. Qualifying Exam/2. Writing/QE Abstract.md

@@ -34,15 +34,8 @@ This method of perturbation generation can be very powerful for robust controlle
 2. Generating unstructured perturbations is hard, but maybe we can use diffusion to do it
 3. Who gives a fuck about validation and verification. Why do we need to do actual samples?
 ## Attempt
-Real world control systems do not operate on nominal plants, but instead control a physical plant that has similar but slightly different dynamics.
-This discrepancy of dynamics is called a perturbation, and can affect controller performance. 
-The amount of perturbation that a control system can tolerate without violating performance or safety requirements is a crucial property to understand for high assurance systems.
-The field of robust control and robustness analysis establishes bounds for the allowable amount of perturbation for a given controller and set of requirements.
-We can know the set of allowable plants that our controller can command, and we can make guarantees about plants within those sets using robust control.
+Real world control systems do not operate on nominal plants, but instead control a physical plant that has similar but slightly different dynamics. This discrepancy of dynamics is called a perturbation, and can affect controller performance.  The amount of perturbation that a control system can tolerate without violating performance or safety requirements is a crucial property to understand for high assurance systems. The field of robust control and robustness analysis establishes bounds for the allowable amount of perturbation for a given controller and set of requirements. We can know the set of allowable plants that our controller can command, and we can make guarantees about plants within those sets using robust control.
 
-What we cannot do is easily validate our guarantees on a real controller. 
-While we have been able to find the set of perturbed plants for decades, being able to create and test a controller with elements from that set is by no means trivial. 
-The most common way of accomplishing this task today is by using structured perturbations, where an engineer traces throughout the system where uncertainty comes from and then establishes probability density functions for each uncertainty, culminating in a probabilistic model that can be sampled to create elements of the set.
-This is an expensive way to create perturbations.
-The other way is by using unstructured perturbations. 
-For this method, some transfer function $\delta$ with a maximum gain is used to augment the nominal plant model. 
+What we cannot do is easily validate our guarantees on a real controller.  While we have been able to find the set of perturbed plants for decades, being able to create and test a controller with elements from that set is by no means trivial.  The most common way of accomplishing this task today is by using structured perturbations, where an engineer traces throughout the system where uncertainty comes from and then establishes probability density functions for each uncertainty, culminating in a probabilistic model that can be sampled to create elements of the set. This is an expensive way to create perturbations. The other way is by using unstructured perturbations.  For this method, some transfer function $\delta$ with a maximum gain is used to augment the nominal plant model. Creating $\delta$ however is an opaque challenge.
+
+We suggest using new technologies to create unstructured perturbations. The diffusion generative model has shown great promise in creating novel and convincing samples