Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport

Author(s)
Lu, Lu; Pestourie, Raphaël; Johnson, Steven G.; romano, Giuseppe
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Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
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Date issued
2022-06-13
URI
https://hdl.handle.net/1721.1/143435
Department
Massachusetts Institute of Technology. Department of Mathematics; Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies
Journal
Physical Review Research
Publisher
American Physical Society (APS)
Citation
Lu, Lu, Pestourie, Raphaël, Johnson, Steven G. and Romano, Giuseppe. 2022. "Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport." Physical Review Research, 4 (2).
Version: Final published version
ISSN
2643-1564
Keywords
General Physics and Astronomy
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