Question
Define the uncertainty before implementation.
Research / Index
Questions before claims.
A working index of the questions behind the builds. Each record follows the evidence trail from question to related system, experiment, recorded result, current conclusion, and next question.
Experiment
Build the smallest useful test and name its boundary.
Evidence
Record measurements, artifacts, and failure modes.
Conclusion
State only what the current evidence supports.
Next question
Use the result to choose the next experiment.
Research records
Questions connected to evidence.
Autonomous control
RL for Control
Question
Research question
How can RL policies control dynamic physical systems while remaining bounded and measurable?
Related work
Connected systems
Experiment
Tabular Q-Learning on CartPole-v1
First bounded control baseline with an explicit evaluation threshold.
Evidence
Recorded outputs
Mean evaluation reward52.0 +/- 36.7
Threshold success0.0%
Best training episode191
Recorded runs1
Measured CartPole evidence only. It does not establish aircraft-control performance.
Conclusion
Current conclusion
The lab has a measurable learned-control baseline, but no evidence yet that the method is adequate for a nonlinear aerospace system.
Next question
What I would test next
What changes when a learned policy acts on a nonlinear aircraft model with actuator limits, disturbances, and explicit stability constraints?
Learning to autonomy
Autonomous control
Question
Research question
How can learned policies make useful decisions in physical systems while remaining measurable, bounded, and understandable?
Related work
Connected systems
Experiment
Deep RL Lunar Lander
First completed Deep RL environment with a recorded policy replay.
Evidence
Recorded outputs
EnvironmentLunarLander
MethodDeep RL
ReplayRecorded
BenchmarkNot recorded
The replay is qualitative evidence of learned behavior; reward and robustness benchmarks are not inferred.
Conclusion
Current conclusion
The progression now includes neural RL, but learned behavior in a benchmark environment is not yet evidence of robust autonomous flight.
Next question
What I would test next
Which evaluation protocol should expose policy stability, constraint violations, and generalization before higher-fidelity control tests?
AI systems engineering
Reliable intelligent systems
Question
Research question
How should autonomous software expose uncertainty, failure modes, and verification boundaries instead of hiding them behind confident outputs?
Related work
Connected systems
Experiment
Execution-validation pass
Generated code is executed for syntax, import, and smoke-test checks.
Evidence
Recorded outputs
ValidationSyntax / import / smoke test
Output classesMeasured vs inferred
Security boundaryNot claimed
This is a validation pattern, not proof of safe autonomous software.
Conclusion
Current conclusion
Model output and measured execution are different evidence classes. Verification begins when generated work is executed against defined checks.
Next question
What I would test next
What isolation, reproducibility, and adversarial testing are required before generated engineering code can be safely reused?
Scientific ML
AI for engineering
Question
Research question
Where can modern AI accelerate engineering work without replacing the measurements, validation, and human judgment that make engineering trustworthy?
Related work
Connected systems
Experiment
Generalized PINN PDE evaluation
Test physics-constrained learning across heat, Poisson, and Burgers cases with reference comparisons.
Evidence
Recorded outputs
Poisson relative L29.96e-5
Burgers max IC error0.0052
PDE examples3 generalized cases
CIPython 3.10-3.12
Project-specific single-configuration measurements, not general PINN performance claims.
Conclusion
Current conclusion
AI becomes more useful in engineering when learned representations remain tied to governing equations, reference solutions, tests, and known scope boundaries.
Next question
What I would test next
Can the same evidence discipline survive when a physics-informed model moves into a higher-dimensional aerospace problem with a stronger numerical baseline?
Applied learning record
RL Laboratory.
Continue from research questions into hands-on reinforcement-learning experiments, recorded results, theory, learning progression, and failure analysis.
Evidence model