Adaptive Monte Carlo augmented with normalizing flows

Author:

Gabrié Marylou12ORCID,Rotskoff Grant M.3ORCID,Vanden-Eijnden Eric4ORCID

Affiliation:

1. Center for Computational Mathematics, Flatiron Institute, New York, NY 10010

2. Center for Data Science, New York University, New York, NY 10011

3. Department of Chemistry, Stanford University, Stanford, CA 94305

4. Courant Institute of Mathematical Sciences, New York University, New York, NY 10012

Abstract

SignificanceMonte Carlo methods, tools for sampling data from probability distributions, are widely used in the physical sciences, applied mathematics, and Bayesian statistics. Nevertheless, there are many situations in which it is computationally prohibitive to use Monte Carlo due to slow “mixing” between modes of a distribution unless hand-tuned algorithms are used to accelerate the scheme. Machine learning techniques based on generative models offer a compelling alternative to the challenge of designing efficient schemes for a specific system. Here, we formalize Monte Carlo augmented with normalizing flows and show that, with limited prior data and a physically inspired algorithm, we can substantially accelerate sampling with generative models.

Publisher

Proceedings of the National Academy of Sciences

Subject

Multidisciplinary

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