Equivariant flow-based sampling for lattice gauge theory

By Gurtej Kanwar, Michael S. Albergo, Denis Boyda, Kyle Cranmer, Daniel C. Hackett, Sébastien Racanière, Danilo Jimenez Rezende, Phiala E. Shanahan. 2020

219 citations recorded by the source corpus.

We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.

Subfields: Computational Theory and Mathematics, Computer Networks and Communications, Condensed Matter Physics, AI for Physical Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Arash Vahdat
2. Alvaro Sanchez-Gonzalez
3. Peter W. Battaglia
4. Tobias Pfaff
5. Jan Kautz
6. Karsten Kreis
7. Ivan Kobyzev

In Computer Networks and Communications

1. Alvaro Sanchez-Gonzalez
2. Peter W. Battaglia
3. Tobias Pfaff
4. Arash Vahdat

In AI for Physical Sciences

1. Peter W. Battaglia
2. Alvaro Sanchez-Gonzalez
3. Karsten Kreis
4. Arash Vahdat

Citations in the corpus

• VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models (2020, background): “…(Kingma et al., 2014; Izmailov et al., 2020) to 3D point cloud generation (Yang et al., 2019), light-transport simulation (Müller et al., 2019), molecular modeling (Sanchez-Lengeling & AspuruGuzik, 2018; Noé et al., 2019) and equivariant sampling in theoretical physics (Kanwar et al., 2020).”
• Learning Mesh-Based Simulation with Graph Networks (2020, background): “High resolution simulations are often very slow, and learned models can provide faster predictions, reducing turnaround time for workflows in engineering and science [14, 6, 44, 18, 1].”
• Normalizing Flows: An Introduction and Review of Current Methods (2020, background): “ation [60], audio generation [27], [53], [77], graph generation [65], reinforcement learning [67], [70], [93], computer graphics [69], and physics [51], [58], [71], [104], [105].”