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However, when simulations of a physical system using tried-and-true scientific techniques are available, researchers get a model of its probability distribution for free.

The MIT team's insight is that this probability distribution also defines a generative model upon which a classifier can be constructed.

The researchers have fed all the variables into the models, and developed a probability distribution for how conditions might develop in the future.

This is then used to generate a probability distribution function which consists of all the feasible and infeasible solutions that are within the constraints of the COP.

Next, the post-processing method transforms the infeasible solutions into feasible ones, leaving the probability distribution with only feasible solutions.