期刊
SCIENCE ADVANCES
卷 5, 期 4, 页码 -出版社
AMER ASSOC ADVANCEMENT SCIENCE
DOI: 10.1126/sciadv.aau6792
关键词
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资金
- National Science Foundation through the Harvard Materials Research Science and Engineering Center [DMR-1420570]
- Alfred P. Sloan Research Foundation
- Computational Science Graduate Fellowship (DOE CSGF)
- JSMF
- Applied Mathematics Program of the U.S. DOE Office of Advanced Scientific Computing Research [DE-AC02-05CH11231]
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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