Corpus ID: 231648295

Introduction to Normalizing Flows for Lattice Field Theory

@article{Albergo2021IntroductionTN,
  title={Introduction to Normalizing Flows for Lattice Field Theory},
  author={M. S. Albergo and Denis Boyda and Daniel C. Hackett and Gurtej Kanwar and Kyle Cranmer and S{\'e}bastien Racani{\`e}re and Danilo Jimenez Rezende and Phiala Shanahan},
  journal={ArXiv},
  year={2021},
  volume={abs/2101.08176}
}
Michael S. Albergo, ∗ Denis Boyda, 3, 4, † Daniel C. Hackett, 4, ‡ Gurtej Kanwar, 4, § Kyle Cranmer, Sébastien Racanière, Danilo Jimenez Rezende, and Phiala E. Shanahan 4 Center for Cosmology and Particle Physics, New York University, New York, NY 10003, USA Argonne Leadership Computing Facility, Argonne National Laboratory, Lemont IL 60439, USA Center for Theoretical Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA The NSF AI Institute for Artificial Intelligence and… Expand

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References

SHOWING 1-10 OF 39 REFERENCES
Gauge equivariant neural networks for quantum lattice gauge theories
TLDR
Gauge equivariant neural-network quantum states are introduced, which exactly satisfy the local Hilbert space constraints necessary for the description of quantum lattice gauge theory with Zd gauge group on different geometries. Expand
Introduction to Quantum Fields on a Lattice
Preface 1. Introduction 2. Path integral and lattice regularisation 3. O(n) models 4. Gauge field on the lattice 5. U(1) and SU(n) gauge theory 6. Fermions on the lattice 7. Low mass hadrons in QCDExpand
Temperature-steerable flows
Boltzmann generators approach the sampling problem in many-body physics by combining a normalizing flow and a statistical reweighting method to generate samples of a physical system's equilibriumExpand
Quantum Chromodynamics on a Lattice
The phenomenological description of hadrons in terms of quarks continues to be successful; the most recent advance was the description of the new particles as built from charmed quarks. Mean-whileExpand
Neural Ordinary Differential Equations on Manifolds
TLDR
It is shown how vector fields provide a general framework for parameterizing a flexible class of invertible mapping on these spaces and it is illustrated how gradient based learning can be performed. Expand
Lattice gauge equivariant convolutional neural networks
TLDR
It is demonstrated that L-CNNs can learn and generalize gauge invariant quantities that traditional convolutional neural networks are incapable of finding. Expand
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
TLDR
Gauge equivariant convolution using a single conv2d call is demonstrated, making it a highly scalable and practical alternative to Spherical CNNs and demonstrating substantial improvements over previous methods on the task of segmenting omnidirectional images and global climate patterns. Expand
Simulation of phi 4 theory in the strong coupling expansion beyond the Ising Limit
Diese Arbeit beschaftigt sich mit der Simulation der phi**4-Theorie mit dem Wurm-Algorithmus, einer Simulationsmethode die sich als sehr effizient bei der Betrachtung kritischer Systeme gezeigt hat.Expand
How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
TLDR
This paper presents a critique of scheduled sampling, a state-of-the-art training method that contributed to the winning entry to the MSCOCO image captioning benchmark in 2015, and presents the first theoretical analysis that explains why adversarial training tends to produce samples with higher perceived quality. Expand
“A and B”:
Direct fabrication of large micropatterned single crystals. p1205 21 Feb 2003. (news): Academy plucks best biophysicists from a sea of mediocrity. p994 14 Feb 2003.
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