Just a quick guess.

Could you improve Monte Carlo simulations by using input from different classified random distributions. What I guess is that machine learning could make something like a ?focus point where the result will converge.

So the idea is to use different distributions like normal and uniform with different parameters and feed it to a machine learning algorithm. Then see if it could be used to make the problem solution converge quicker.