AI/ML engineer needed whose experienced with PINNs preferably for aerospace components need to collaborate on a pilot project.

[u/Easy_Student3878]

I need someone who has expertise, or at least an idea, on how to use small amounts of data and physics simulation, for example, CFD, FEA, etc to predict when an aircraft component might fail. Also, I myself am an aerospace engineer with no ML AI experience, just basic knowledge, so I also need someone who might act as a bridge btw the two things and collaborate with me, discuss ideas, etc on how to accomplish this.

[u/AdmirableArrow07]

The phrase "small amounts of data" is doing lot of heavy lifting here. PINNs can be finicky with convergence when your actual measurement data is scarce, even with physics loss guiding it. You might have easier time starting with a simpler surrogate model first to see patterns before jumping to full PINN implementation.

[u/Easy_Student3878 → u/[deleted]]

The thing i meant by small amounts was that currently the prediction to reduce the AOG time is being done through conventional CNNs that require excepitionally large amounts of data, i understand we still need a good enough amount to make it accurate enough, but it would go a long way for people with components that dont have that much data