# 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

#### 7 Citations

Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

- Computer Science, Physics
- 2021

A continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics is proposed, based on a shallow design and incorporates the symmetries of the problem, and systematically outperforms a realNVP baseline in sampling efficiency. Expand

Efficient Modelling of Trivializing Maps for Lattice $\phi^4$ Theory Using Normalizing Flows: A First Look at Scalability

- Physics
- 2021

General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a dramatic reduction in efficiency as the system being studied is driven towards a critical point through, for example, taking… Expand

Gauge covariant neural network for 4 dimensional non-abelian gauge theory

- Physics
- 2021

We develop a gauge covariant neural network for four dimensional non-abelian gauge theory, which realizes a map between rank-2 tensor valued vector fields. We find that the conventional smearing… Expand

Normalizing flows for random fields in cosmology

- Physics
- 2021

Normalizing flows are a powerful tool to create flexible probability distributions with a wide range of potential applications in cosmology. Here we are studying normalizing flows which represent… Expand

Deep generative modeling for probabilistic forecasting in power systems

- Computer Science
- ArXiv
- 2021

Through comprehensive empirical evaluations using the open data of the Global Energy Forecasting Competition 2014, it is demonstrated that the methodology is competitive with other state-of-the-art deep learning generative models: generative adversarial networks and variational autoencoders. Expand

A Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market

- Mathematics, Computer Science
- IEEE Transactions on Sustainable Energy
- 2021

This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small noninterconnected grids with a probabilistic forecast-driven strategy. Expand

A deep generative model for probabilistic energy forecasting in power systems: normalizing flows

- Computer Science, Engineering
- 2021

This paper proposes to present to the power systems forecasting practitioners a recent deep learning technique, the normalizing flows, to produce accurate scenario-based probabilistic forecasts that are crucial to face the new challenges in power systems applications. Expand

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