Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
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Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

  • Author(s): Hoffmann, Jordan
  • Bar-Sinai, Yohai
  • Lee, Lisa
  • Andrejevic, Jovana
  • Mishra, Shruti
  • Rubinstein, Shmuel M
  • Rycroft, Chris H
  • et al.
Abstract

Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data. However, these techniques are ostensibly inapplicable for experimental systems where data is scarce or expensive to obtain. Here we introduce a strategy to resolve this impasse by augmenting the experimental dataset with synthetically generated data of a much simpler sister system. Specifically, we study spontaneously emerging local order in crease networks of crumpled thin sheets, a paradigmatic example of spatial complexity, and show that machine learning techniques can be effective even in a data-limited regime. This is achieved by augmenting the scarce experimental dataset with inexhaustible amounts of simulated data of rigid flat-folded sheets, which are simple to simulate and share common statistical properties. This significantly improves the predictive power in a test problem of pattern completion and demonstrates the usefulness of machine learning in bench-top experiments where data is good but scarce.

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