Speaker
Simone Bacchio
Description
In our recent work, [arXiv:2212.08469], we have presented a new approach for trivializing flows that starts from the perturbative construction of trivializing maps by Lüscher and improves on it by learning. The resulting continuous normalizing flow model can be implemented using common tools of lattice field theory and requires several orders of magnitude fewer parameters than state-of-the-art deep learning approaches. Specifically, our model can achieve competitive performance with as few as 14 parameters while existing deep-learning models have around 1 million parameters for SU(3).
