The saga continues… new paper using AI /ML for theoretical nuclear physics with the crew at MIT and @DeepMind.
“Flow-based sampling in the lattice Schwinger model at criticality”
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arxiv.org/abs/2202.11712 We bring together two main threads of recent research. The first has to do with incorporating the symmetries found in fundamental particle physics (Lie groups) into a type of deep generative model called normalizing flows

https://twitter.com/KyleCranmer/status/1239346481942986757

Very excited to share our newest paper
combining machine learning & physics. We develop normalizing flows that impose the elaborate of symmetry groups you find in fundamental particle physics.
It's a beautiful mix of math, ML, and physics
arxiv.org/abs/2008.05456 It took several months to work through a series of challenges. Shout out to the team:
Denis Boyda, Gurtej Kanwar, @sracaniere, @DaniloJRezende,
@msalbergo, @kylecranmer, Daniel Hackett, Phiala Shanahan
arxiv.org/abs/2008.05456

Johann & I released v2 of our paper "Flows for simultaneous manifold learning and density estimation" with more experiments. We dubbed the class of model ℳ-Flows Here you see the flow learning the 2d manifold and the density for the Lorenz attractor! 1/n
arxiv.org/abs/2003.13913 But there's more. We did our first experiments with images. We used a StyleGAN to produce images that lie on 2-D and 64-D manifolds embedded in a 64x64x3 dimensional data space. Here are some images generated from the model. We have a tractable likelihood for these images! 2/n