Lawrence Berkeley National Laboratory
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.
Published Web Locationhttps://doi.org/10.1126/sciadv.aau6792
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.