[hodgehog11]

I would argue there are more applications overall where Gaussian processes are superior, as most scientific applications have smaller data sets. Not everything has enough data to take advantage of feature learning in NNs. They are generally reliable, interpretable, and provide excellent uncertainty estimates for free. They can be made to be multiscale, achieving higher precisions as a function approximator than most other methods. Plus, they can exhibit reversion to the prior when you need that.

Another example where it is used is for emulating outputs of an agent-based model for sensitivity analyses.