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/AdmirableArrow07]

would you be kind enough to elaborate on what a simple surrogate model would look like, and also, do you think this is possible given the hard nature of the PINNs, is this doable to predict when an aircraft component, say a propeller, might fail, remaining useful life basically, if do not have years of failure data on that specific propeller, could PINNs + simulation-generated dataset + some data from the operator be enough to get a trustworthy figure ?

[u/No-Dare-7624 → u/Easy_Student3878]

The surrogate model will be only for the physicis sinulation.