Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Name
PhysRevResearch.4.023210.pdf
Size
2.35 MB
Format
Adobe PDF
Checksum (MD5)
1ca236f3762b7462d94d8647ccc66619
Author(s)
Lu, Lu
Pestourie, Raphaël
Johnson, Steven G.
romano, Giuseppe
Date Issued
June 13, 2022
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
Subjects
General Physics and Astronomy
MIT Department
Massachusetts Institute of Technology. Department of Mathematics
Massachusetts Institute of Technology. Institute for Soldier Nanotechnologies
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1103/physrevresearch.4.023210
