Aug 8 – 13, 2022
Hörsaalzentrum Poppelsdorf
Europe/Berlin timezone

Stochastic normalizing flows for lattice field theory

Aug 8, 2022, 2:20 PM
CP1-HSZ/1.004 (CP1-HSZ) - HS7 (CP1-HSZ)

CP1-HSZ/1.004 (CP1-HSZ) - HS7


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Oral Presentation Algorithms (including Machine Learning, Quantum Computing, Tensor Networks) Algorithms


Elia Cellini


Normalizing flows (NFs) are a class of machine-learning algorithms that can be used to efficiently evaluate posterior approximations of statistical distributions. NFs work by constructing invertible and differentiable transformations that map sufficiently simple distributions to the target distribution, and provide a new, promising route to study quantum field theories regularized on a lattice. In this contribution, based on our recent work [arXiv:2201.08862], I explain how to combine NFs with stochastic updates, demonstrating that this theoretical framework is the same that underlies Monte Carlo simulations based on Jarzynski’s equality, and present examples of applications for the evaluation of free energies in lattice field theory.

Primary authors

Prof. Michele Caselle (Università di Torino/INFN Torino) Elia Cellini Dr Alessandro Nada (Università di Torino/INFN Torino) Prof. Marco Panero (Università di Torino/ INFN Torino)

Presentation materials