A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, redu...

Full description

Bibliographic Details
Main Authors: Duffy, K. (Author), Ganguly, A.R (Author), Nemani, R.R (Author), Vandal, T.J (Author), Wang, W. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
Atmospheric modeling
Bayes method
Bayes methods
Bayesian
Bayesian deep learning
Bayesian deep learning (DL)
Computational efficiency
Computational modeling
Computational modelling
Data models
Deep learning
Efficiency
emulation
Emulation
Mathematical models
Numerical models
Remote sensing
surrogate modeling
Surrogate modeling
Uncertainty
Uncertainty analysis
uncertainty quantification.
Uncertainty quantification.
Uncertainty quantifications
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items