They defined a neural network operation which can in principal learn to approximate certain equations used in Nuclear physics and other quantum field theories.

They defined a neural network operation which can in principal learn to approximate certain equations used in Nuclear physics and other quantum field theories. Approximating QFT/Nuclear physics is a very difficult task given current algorithms, so this opens potential doors for learning "faster" approximations which allow simulating more complex systems. Of course, defining the error in this approximation becomes quite difficult. They claim the following in their paper. 1. A theoretical proof that the new operation can approximate the relevant equations. 2. An evaluation of the model trained against a traditional simulation on both equal sized and "larger sized" problems. Unfortunately they only summarize this as a "High degree of accuracy" and present charts. I'd love to see a summary table of results with a final numeric accuracy evaluation.