Sounds like the type of problem a Gaussian Process model would be well suited to as it considers a level of noise within the training data in the first place.

It’s usage however is very dependent on the amount and type of data you’re working with so I think u/gBoostedMachinations has the best approach to this problem without knowing more about your data.

Thanks for sharing the link! This’ll actually work really nicely for a paper I’m writing!

Yo! All good ideas so far but have you considered using a smaller experimental design / non grid based experimental design?

For only 2 hyper parameters you likely could get away with using fewer points and the building a model to better understand their relationship relative to your target (however you’re evaluating your model in your original grid search).

Best of luck to you!