Emulation, a.k.a. surrogate modelling. In this context, it means reducing complicated physics-driven simulations to simpler/or faster ones using ML techniques. Especially popular in the ML for physics pipeline. I have mostly done this in the context of surrogate optimisation for experiments.

A recent, hyped paper that exemplifies this approach is Kasim et al. (2020), which (somewhat implicitly) uses arguments from Dropout ensembling to produce quasi-Bayesian emulations of notoriously slow simulations.
Does it actually work?
And if it *does* well quantify posterior predictive uncertainty, can it estimate other posterior uncertainties?

Emukit (Paleyes et al. 2019) is a toolkit which generically wraps ML models for emulation purposes.

ML PDEs might be a useful thing.

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