Abstract
Machine learning has gained widespread attention as a powerful tool to identify structure in complex, highdimensional 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.
Original language | American English |
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Article number | eaau6792 |
Journal | Science advances |
Volume | 5 |
Issue number | 4 |
DOIs | |
State | Published - 2019 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2019 The Authors.