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Open Access Publications from the University of California

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 M;
  • Andrejevic, Jovana;
  • Mishra, Shruti;
  • Rubinstein, Shmuel M;
  • Rycroft, Chris H
  • et al.

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 are 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 considerably improves the predictive power in a test problem of pattern completion and demonstrates the usefulness of machine learning in bench-top experiments where data are good but scarce.

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