I find it hard to believe that most people feel comfortable with 25 – 28.3 °C, how to come up with this numbers? Based on the link above? It seems rather hot. Also it would depend what the outdoor temperature is. But different people have different preferences
https://comfort.cbe.berkeley.edu/ (comfort calculator)
how flexible was Botorch for your task and is it difficult to map it to a Botorch specific format. And want kind of understanding did you have about the underlying cost/objective function and what kind of surrogate model did you used? Just basic GP(gaussian process) ?
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.
It's also very useful for simulation-based optimization. As an example, we use it extensively for the design of particle accelerators, where the simulations are typically expensive and need to run on supercomputers. We have built our own library[0] for enabling this, which in the end uses BoTorch (through Ax[1]) under the hood.
