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Zettelkasten/Fleeting Notes/Editing/thesis_ideas_notes.md
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# Notes on [[thesis-ideas-2025-07-30]]
What needs done:
- [X] 1 needs edited and reviewed
- [X] Review outcomes. I really don't like outcome
number 1.
- [X] Review and edit 2
- [ ] Review and edit 3
- [ ] Write an impact section
- [ ] Review and edit 4
- [ ] Needs more goal
- [ ] Review and edit 5
- [ ] Review and edit 6

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Zettelkasten/Fleeting Notes/Journal/2025_08_07.md
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---
---
# 2025-08-06
# 2025-08-07
Today I have not gotten too much done, but I did get here
early today. Today, I'll sell the truck and work on Sam's
car. I saw Patrick today too, which was nice. Robert's stuff

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Zettelkasten/Fleeting Notes/Journal/2025_08_10.md Normal file
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---
---
# 2025-08-10
Today I'm finishing the thesis ideas, for real this time.
I'm also going to get a model of a heat exchanger working.

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Zettelkasten/Permanent Notes/thesis-ideas.md
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@ -5,38 +5,49 @@ suggested I write down 6 ideas, and from them we shall
figure out a possible topic idea that I can really start
working on.
I used ChatGPT to do some of the heavy lifting based on the
papers I've been reading, and leveraged the 'deep research'
feature. Here are some of my favorite ideas, broken down
into goals, outcomes, impact, and related papers.
___________________________________________________________
## **Integrating Shielding into Nuclear Power Control**
### Goal:
The goal of this research is develop machine learning
enabled control algorithims for nuclear power applications
that incoporate shielding: a formal guarantee of adherence
to system specifications without augmenting the machine
learning process.
The goal of this research is to use temporal logic shielding
to do online safety assurance of a machine learning (ML)
based controller for a reactor control system (RCS). While
ML-based controllers can outperform traditional rule-based
or PID control systems, their lack of provable safety
guarantees has prevented their deployment in nuclear control
applications. This work proposes to integrate shielding into
a ML-based RCS to provide a strong safety guarntee.
Shielding uses temporal logic specifications to define an
area in the state space that is safe. If the ML controller's
predicted actions leave this space or violate a
specification, the shield intervenes and engage a
safety-oriented fallback controller. This way, safety
guarantees are still provided by the fallback controller,
but the ML controller is free to operate within the verified
safe region..
### Outcomes:
For this research to be successful, I will accomplish the
following:
1. Identify key controllers in a nuclear power context with
the most benefit from using an ML-based controller
1. Translate regulatory and system level requirements into a
temporal logic specification to synthesize a controller
'shield'. This shield monitors the ML controller and
intervenes whenever a requirement is predicted to be
violated.
2. Translate regulatory and system level requirements into a
formal specification to synthesize a controller 'shield'.
This shield monitors the ML controller and intervenes
whenever a requirement is predicted to be violated.
2. Design a verified fallback controller against the same
verification requirements that can either return the
reactor to the safe state space or safely shut down the
reactor.
3. Evaluate performance of the ML controller with attached
shield, while assessing the amount of shield useage for
different operating scenarios (power up, shut down, regular
load following)
shield, while assessing shield intervention frequency for
different operating scenarios (power up, shut down, regular
load following).
### Impact:
@ -45,17 +56,16 @@ rule-based controllers by adapting to nonlinear dynamics,
optimizing over multi-objective cost functions, and changing
plant conditions. But, these ML controllers are often
*unexplainable*, meaning that their global behavior is not
easily understood.This unexplainability prevents ML based
controllers from being used in high-assurance usecases such
as nuclear power. Shielding can address this issue, by
providing a formal runtime assurance, allieviating the
burden of explainability away from the machine learning
algorithm. This work would further bring regulatory
requiremnts into the formal design of control systems and
help bridge the gap between high assurance systems and the
start of the art in control.
easily understood. This prevents ML based controllers from
being used in high-assurance usecases such as nuclear power.
Shielding can address this issue, by providing a formal
runtime assurance, alleviating the burden of explainability
away from the machine learning algorithm. This work would
further bring regulatory requiremnts into the formal design
of control systems and high
### Relevant Papers
[[safe-reinforcement-learning-via-shielding]]
[[evaluating-robustness-of-neural-networks-with-mixed-integer-programming]]
@ -64,50 +74,71 @@ ___________________________________________________________
## **Formally Verified Neural Network Control of Control Rod System**
### Goals:
The goal of this research is to use formal methods to ensure that
a neural network based control rod controller will never violate
safety guarantees of a reactor trip system. To do this, a
satisfiability modulo theory method will be applied to
exhaustively search the network for potential failure modes.
The goal of this resarch is to use formal methods to verify
safety properties of neural network controller for a reactor
control system. Neural network based controllers are able to
efficiently control nonlinear systems with complex
objectives, but by their nature are are opaque systems whose
behavior is not easily analyzed. That being said, once a
neural network is trained, it is an entirely deterministic
system. Recent techniques using satisfiability modulo theory
(SMT) or mixed integer linear programming (MILP) encodings
of neural networks have been used to verify neural network
controller behavior adheres to constraints for fixed input
spaces. This work will adapt these methods to be used with
nuclear-specific safety constraints and enable provably safe
neural network control for the nuclear industry.
### Outcomes:
If this research is successful, I will have accomplished the
For this research to be successful, I will accomplish the
following:
- Build a neural network controller for real time control of a
control rod system.
1. Build a neural network controller for real time control
of a control rod system.
- Formalize safety guarantees of shutdown margin in a
satisfiability modulo theory embedding
2. Encode shutdown margin safety requirements into a SMT or
MILP framework.
- Formally verify that the neural network based controller will
not violate any shutdown margin restrictions
3. Formally verify that the neural network based controller
will not violate any shutdown margin restrictions while
still meeting operational goals.
### Impact:
SMT solvers and MILP formulations have been applied to neural
networks to ensure that the network is resilient to input
perturbations. I think we can expand this to more general
considerations of the state space, especially when there are a
relatively small number of states such as in power contexts. The
benefit of this system is that we would get closer to saying
neural network based systems can be high assurance for physical
systems.
SMT and MILP methods of verifying neural networks have been
previously applied to classification problems, but have not
been utilized to check neural network controllers for
adherence to cyber-physical system requirements. Critical
infrastructure control such as nuclear power is an ideal
candidate for these methods to be utilized, however, because
the systems being controlled have a bounded and
well-characterized state space compared to other neural
network controller usecases (autonomous flight, autonomous
driving, image classification, etc.). This work, if
successful, will help ease tensions about neural network
controllers' safety for critical infrastructure, and allow a
new class of provably safe control architectures to be used
in high assurance systems.
### Related Papers:
[[reluplex-an-efficient-smt-solver-for-verifying-deep-neural-networks]]
[[evaluating-robustness-of-neural-networks-with-mixed-integer-programming]]
[[formal-verification-of-neural-network-controlled-autonomous-systems]]
___________________________________________________________
## **Temporal Logic Specifications for Autonomous Controller Synthesis**
### Goals:
The goal of this program is to use temporal logic
specifications to procedurally generate autonomous
supervisory controllers for a reactor system.
### Outcomes:
If this research is successful, I will have accomplished the
following:
@ -129,11 +160,13 @@ ___________________________________________________________
## **Formally Verified Runtime Monitoring and Fallback**
### Goals:
If this research is successful, we will be able to generate
autonomous controller shields that provably adhere to specifications
written with temporal logic.
### Outcomes:
- Create an intermediary shield that mediates signals between an
optimal control system and the physical plant (MODBUS)?
@ -145,6 +178,7 @@ shield components will not allow an arbitrary controller to
reach an unsafe state.
### Impact:
Shielding is one of the preeminent ways to do safe machine
learning controllers. Instead of putting the proof burden on
the machine learning component, shielding creates a safe
@ -162,6 +196,7 @@ engineers of these systems to quickly and clearly implement
a shield, without all of the cumbersome derivation.
### Related Papers:
[[on-using-real-time-reachability-for-the-safety-assurance-of-machine-learning-controllers]]
[[enhancing-cyber-physical-system-dependability-via-synthesis-challenges-and-future-directions]]
[[safe-reinforcement-learning-via-shielding]]
@ -172,6 +207,7 @@ ___________________________________________________________
(8)
### Goals:
The goal of this research is to use machine learning to
identify system faults of a reactor control system during
runtime. A digital twin will be compared to measurements
@ -181,6 +217,7 @@ that safety strategic decisions about the plant can be made
autonomously.
### Outcomes:
For this research to be successful, I will accomplish the
following:
@ -199,6 +236,7 @@ control modes rather than only responding with reactor
shutdown.
### Impact:
The nuclear energy industry's largest expense is operations
and maintenance (O&M). These costs include typical reactor repair
and refueling, the labor involved to complete such
@ -229,17 +267,19 @@ Finally reactor safety can be improved by greater autonomy
yada yada find some reasons to back this up.
### Related Papers:
___________________________________________________________
## **Verified Adaptive Control**
### Goals:
The goal of this research is to create an adaptive controller
that can adjust to system dynamics changes over time to maintain
an optimal control, while using formal methods to provide strong
safety guarantees about the malleable control law.
### Outcomes:
For this research to be successful, I will accomplish the
following:
@ -259,6 +299,7 @@ suspended in the salt I'd assume chemistry is pretty strictly
controlled. I'm sure I can find other examples.
### Impact:
Certain reactor control systems are already automatic systems,
such as constant temperature or pressure controls for operating
at steady state. These simple controllers are able to follow load
@ -274,5 +315,3 @@ of safety in order to be attractive to the nuclear industry.
### Related Papers: