[fastneutron]

It wasn’t terribly difficult once we got a feel for the underlying API. GP surrogates work out of the box, but you can plug in other kinds as well. Same goes for acquisition functions.

As far as the objective, usually we’re calling an external physics-based solver (e.g. finite elements) and post-processing the solution to get the quantity we’re trying to optimize. There’s almost never any gradient information, so Bayesian optimization winds up being the method of choice.