When my collaborators and I published our LQCD computation of the nucleon axial coupling I wrote this http://evanberkowitz.com/2018/05/30/gA.html http://evanber

When my collaborators and I published our LQCD computation of the nucleon axial coupling I wrote this http://evanberkowitz.com/2018/05/30/gA.html http://evanberkowitz.com/2018/05/30/gA.html though I did not get into the technical details of gauge invariance. We can use neural networks in some steps but not in others. The main use seems to be in the generation of gauge configurations (states of the Markov chains we use). We can use neural networks there because we alway do our Metropolis-Hastings accept/reject step according to the ‘true’ action, which corrects any numerical errors introduced by the ML approximation. There are different approaches for this. I’ve been working on incorporating ML into HMC; see eg. https://arxiv.org/abs/2006.11221 https://arxiv.org/abs/2006.11221. For an approach based on normalizing flows, search for Shanahan eg al. Happy to answer questions, but it’s late and I’m about to go to sleep. Feel free to ask away and I will do my best in the morning. Or email me.