Learning to sample better

Michael S Albergo, Eric Vanden-Eijnden

Research output: Contribution to journalArticlepeer-review

Abstract

These lecture notes provide an introduction to recent advances in generative modeling methods based on the dynamical transportation of measures, by means of which samples from a simple base measure are mapped to samples from a target measure of interest. Special emphasis is put on the applications of these methods to Monte-Carlo (MC) sampling techniques, such as importance sampling and Markov Chain Monte-Carlo schemes. In this context, it is shown how the maps can be learned variationally using data generated by MC sampling, and how they can in turn be used to improve such sampling in a positive feedback loop.

Original languageEnglish (US)
Article number104014
JournalJournal of Statistical Mechanics: Theory and Experiment
Volume2024
Issue number10
DOIs
StatePublished - Oct 31 2024

Keywords

  • deep learning
  • machine learning
  • mixing
  • numerical simulations
  • sampling algorithms

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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