One platform for all researcher needs
AI-powered academic writing assistant
Your #1 AI companion for literature search
AI tool for graphics, illustrations, and artwork
Unlock unlimited use of all AI tools with the Editage Plus membership.
Explore Editage Plus
Audio Papers
Ask R Discovery
Translation
Auto-sync
Collaboration
Open Access
PDF Availablehttps://doi.org/10.1103/physrevfluids.6.124602
Copy DOI
Export
Save
Share
Cite Journal: Physical Review Fluids |  Publication Date: Dec 6, 2021 |
 Citations: 15 |
Affiliation: University of Michigan–Ann Arbor
We present a data-driven framework that uses very limited data to learn generalizable bypass transition models. Trained on just two flat plate cases, the augmented model is shown to generalize to other flat plate cases and turbomachinery configurations.
Free) 
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
