Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer-Reference-Cited by-同舟云学术

Deep Learning for Subgrid‐Scale Turbulence Modeling in Large‐Eddy Simulations of the Convective Atmospheric Boundary Layer

Published:2022-05 Issue:5 Volume:14 Page:
ISSN:1942-2466
Container-title:Journal of Advances in Modeling Earth Systems
language:en
Short-container-title:J Adv Model Earth Syst

Author:

Cheng Yu¹^ORCID,Giometto Marco G.²,Kauffmann Pit³,Lin Ling³,Cao Chen³,Zupnick Cody³,Li Harold¹,Li Qi⁴^ORCID,Huang Yu¹,Abernathey Ryan⁵^ORCID,Gentine Pierre¹⁵^ORCID

Affiliation:

1. Department of Earth and Environmental Engineering Columbia University New York NY USA

2. Department of Civil Engineering and Engineering Mechanics Columbia University New York NY USA

3. Capstone Project at Data Science Institute Columbia University New York NY USA

4. School of Civil and Environmental Engineering Cornell University Ithaca NY USA

5. Department of Earth and Environmental Sciences Columbia University Palisades NY USA

Funder

National Science Foundation

Office of Science

Publisher

American Geophysical Union (AGU)

Subject

General Earth and Planetary Sciences,Environmental Chemistry,Global and Planetary Change

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2021MS002847

Reference83 articles.

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4. Bar‐Sinai Y. Hoyer S. Hickey J. &Brenner M. P.(2018). Data‐driven discretization: A method for systematic coarse graining of partial differential equations arXiv preprint arXiv:1808.04930.

5. Experimental analysis of a confined transitional plume with respect to subgrid-scale modelling

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