thesis ideas

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## Discussion Cheat Sheet
Chat helped with this
### Temporal Logic Specifications for Autonomous Controller
Synthesis
- **Feasibility:** ★★★★★
- **Impact:** ★★★★☆
- **Merit:** ★★★★★
**Scope Boundaries:** Focus on one subsystem (e.g., rod
supervisory control), one specification language, and
existing synthesis tools (TLA+, FRET, Strix).
**Key Risk:** State space explosion during synthesis could
make controller generation intractable.
**Mitigation Strategy:** Use bounded abstractions,
compositional synthesis, and validate the synthesized
controller on a high-fidelity simulation before scaling up.
---
### Formally Verified Runtime Monitoring and Fallback
- **Feasibility:** ★★★★★
- **Impact:** ★★★★☆
- **Merit:** ★★★★☆
**Scope Boundaries:** Single primary controller with one
fallback controller, one LTL specification set, and
integration with ARCADE.
**Key Risk:** Limited novelty if scoped too narrowly or
perceived as a straightforward engineering integration.
**Mitigation Strategy:** Emphasize automation of
specification-to-monitor translation, nuclear-specific
verification, and proof artifact generation to show novelty.
---
### Verified Adaptive Control
- **Feasibility:** ★★★★☆
- **Impact:** ★★★★☆
- **Merit:** ★★★★☆
**Scope Boundaries:** One subsystem (rod control), one
adaptation method, runtime contract monitoring only.
**Key Risk:** Over-scoping to multiple adaptation targets
or attempting plant-wide adaptive control.
**Mitigation Strategy:** Pick representative degradation
types (e.g., HX fouling, pump efficiency drop); limit
adaptation to parameter tuning inside pre-verified safe
envelopes.
---
### Integrating Shielding into Nuclear Power Control
- **Feasibility:** ★★★★☆
- **Impact:** ★★★★☆
- **Merit:** ★★★★☆
**Scope Boundaries:** One ML control task (e.g., startup or
load-following), one shield synthesis approach from temporal
logic.
**Key Risk:** Regulatory and industry reluctance toward ML
in safety-critical nuclear applications.
**Mitigation Strategy:** Demonstrate shielding benefits for
both ML and conventional controllers to broaden acceptance.
---
### Improved: Data-Driven Fault Detection Using
High-Assurance Digital Twins
- **Feasibility:** ★★★★☆
- **Impact:** ★★★★☆
- **Merit:** ★★★★☆
**Scope Boundaries:** Limit to 34 high-impact fault types
(e.g., secondary coolant loss, HX fouling, sensor drift),
residual-based detection with physics-informed models.
**Key Risk:** Scope creep into too many fault scenarios or
overly complex ML methods.
**Mitigation Strategy:** Focus on explainable,
physics-informed detection; tie mitigation responses
directly to NRC-aligned safety procedures.
---
### Formally Verified Neural Network Control of Control Rod
System
- **Feasibility:** ★★★☆☆
- **Impact:** ★★★★☆
- **Merit:** ★★★☆☆
**Scope Boundaries:** Small, well-structured NN
architecture; bounded state space; one primary safety
property (shutdown margin).
**Key Risk:** Scalability issues in SMT/MILP verification
for larger or more complex networks.
**Mitigation Strategy:** Constrain network size and
complexity; limit verification domain to tractable operating
regions; focus on proof-of-concept that shows
nuclear-specific applicability.