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Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive

Catalog Data

Author:
Zhong, Weishun  Search this
Gold, Jacob M.  Search this
Marzen, Sarah  Search this
England, Jeremy L.  Search this
Yunger Halpern, Nicole  Search this
Object Type:
Smithsonian staff publication
Year:
2021
Citation:
Zhong, Weishun, Gold, Jacob M., Marzen, Sarah, England, Jeremy L., and Yunger Halpern, Nicole. 2021. "Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive." Scientific Reports, 11 9333. https://doi.org/10.1038/s41598-021-88311-7.
Identifier:
164182
DOI:
https://doi.org/10.1038/s41598-021-88311-7
ISSN:
2045-2322
Data source:
Smithsonian Libraries and Archives
EDAN-URL:
edanmdm:slasro_164182