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?