RT Journal Article
SR Electronic
T1 Machine learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets
JF Science Advances
JO Sci Adv
FD American Association for the Advancement of Science
SP eaau6792
DO 10.1126/sciadv.aau6792
VO 5
IS 4
A1 Hoffmann, Jordan
A1 Bar-Sinai, Yohai
A1 Lee, Lisa M.
A1 Andrejevic, Jovana
A1 Mishra, Shruti
A1 Rubinstein, Shmuel M.
A1 Rycroft, Chris H.
YR 2019
UL http://advances.sciencemag.org/content/5/4/eaau6792.abstract
AB 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.