This can be a useful connection, but I believe it's worth fleshing out in case it is misleading What's the alternative to using a NN for position evaluation? I

This can be a useful connection, but I believe it's worth fleshing out in case it is misleading What's the alternative to using a NN for position evaluation? I can think of two: 1. Do minimax search until you have a winner or a stalemate. Then you have the exact value of the position. Well, this is the problem we're actually trying to solve to begin with, and it's also impractical to do for chess and any interesting game. This is what necessitates an approximation to position evaluation. 2. A human expert writes a position evaluation function. It determines a huge handful of features, a simple example being how many pieces I have - how many pieces you have, and some way to combine those features into a score. In surrogate modeling, you can get ground-truth data to evaluate your approximation against. You're approximating another model that you can compute, it's just too slow for practical use. In chess, we don't know THE position evaluation function. We can certainly get data about it, but we don't know it in the same way we know PDE models. To be clear, I am not saying we _know_ the PDE models are accurate with respect to reality. That's the science part, to determine if the model arising from empirical evidence or first-principles that are themselves arising from empirical evidence, actually summarizes empirical evidence.