https://www.nationalacademies.org/our-work/realizing-opportu...
OP is a long read and suffers from being something of a catalog of promising ideas -- so it's hard to digest. But from all the examples given, you do get the feeling there's something really powerful and new coming into being that combines the large datasets you can obtain from simulation with machine learning/stats tools to build models, find posteriors, etc.
In science applications of remote sensing, my group at NASA/JPL has obtained huge speedups from replacing computationally-expensive physics-based forward models with emulators based on ANNs or GPs. In terms of OP, this is "Motif 2" (surrogates/emulators) combined with fitting based on "Motif 7" (differentiable programming).
You build a training set using selected runs of the physics-based forward model, and train an emulator that links (say) at-sensor radiances with ground or atmosphere conditions. Roughly a 400-variable to 400-variable function emulator.
Then you use the emulator for (say) each 30m x 30m pixel of a global satellite dataset, instead of running the forward model for each such pixel. Replacing the expensive forward model can reduce workload for a NASA science mission by factors of 10 or more.
This capability really didn't exist 10 years ago, even though a lot of the algorithms were almost there, and the computational capacity was almost there. It's some kind of capacity-building thing where you have to have competence across several kinds of "data science" in order to make a system that's effective.