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History-Based, Bayesian, Closure for Stochastic Parameterization:
  Application to Lorenz '96

History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96

26 October 2022
Mohamed Aziz Bhouri
Pierre Gentine
    AI4TS
    AI4CE
ArXivPDFHTML

Papers citing "History-Based, Bayesian, Closure for Stochastic Parameterization: Application to Lorenz '96"

4 / 4 papers shown
Title
Machine Learning for Stochastic Parameterization: Generative Adversarial
  Networks in the Lorenz '96 Model
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model
D. Gagne
H. Christensen
A. Subramanian
A. Monahan
AI4CE
BDL
41
139
0
10 Sep 2019
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
273
5,660
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,136
0
06 Jun 2015
A Differential Equation for Modeling Nesterov's Accelerated Gradient
  Method: Theory and Insights
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
Weijie Su
Stephen P. Boyd
Emmanuel J. Candes
105
1,152
0
04 Mar 2015
1