PIRSA:18040132 Scientific Series Condensed Matter

DOI10.48660/18040132

Series: Machine Learning Initiative

van Nieuwenburg, E. (2018). Learning a phase diagram from dynamics. Perimeter Institute. https://pirsa.org/18040132

van Nieuwenburg, Evert. Learning a phase diagram from dynamics. Perimeter Institute, Apr. 23, 2018, https://pirsa.org/18040132 

@misc{ pirsa_PIRSA:18040132,
  doi = {10.48660/18040132},
  url = {https://pirsa.org/18040132},
  author = {van Nieuwenburg, Evert},
  keywords = {Condensed Matter},
  language = {en},
  title = {Learning a phase diagram from dynamics},
  publisher = {Perimeter Institute},
  year = {2018},
  month = {apr},
  note = {PIRSA:18040132 see, \url{https://pirsa.org}}
}

Abstract

Time series data contains useful information on the phase of a system. Here we propose the use of recurrent neural networks (LSTM) to learn and extract such information in order to classify phases and locate phase boundaries. We demonstrate this on a many-body localized model, and attempt to interpret the learned behavior by looking at individual LSTM cells. We also discuss the validity of the learned model and investigate its limits.

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