Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods-Reference-Cited by-同舟云学术

Training Physics‐Based Machine‐Learning Parameterizations With Gradient‐Free Ensemble Kalman Methods

Published:2022-08 Issue:8 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:

Lopez‐Gomez Ignacio¹^ORCID,Christopoulos Costa¹^ORCID,Langeland Ervik Haakon Ludvig¹,Dunbar Oliver R. A.¹^ORCID,Cohen Yair¹^ORCID,Schneider Tapio¹²^ORCID

Affiliation:

1. Department of Environmental Science and Engineering California Institute of Technology Pasadena CA USA

2. Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA

Funder

Resnick Sustainability Institute for Science, Energy and Sustainability, California Institute of Technology

Heising-Simons Foundation

National Science Foundation

Defense Advanced Research Projects Agency

Publisher

American Geophysical Union (AGU)

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2022MS003105

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4. An Intercomparison of Large-Eddy Simulations of the Stable Boundary Layer

5. Enforcing Analytic Constraints in Neural Networks Emulating Physical Systems

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