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Using Probabilistic Machine Learning to Better Model Temporal Patterns
  in Parameterizations: a case study with the Lorenz 96 model

Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model

28 March 2022
R. Parthipan
H. Christensen
J. S. Hosking
Damon J. Wischik
    AI4CE
ArXivPDFHTML

Papers citing "Using Probabilistic Machine Learning to Better Model Temporal Patterns in Parameterizations: a case study with the Lorenz 96 model"

3 / 3 papers shown
Title
Don't Waste Data: Transfer Learning to Leverage All Data for
  Machine-Learnt Climate Model Emulation
Don't Waste Data: Transfer Learning to Leverage All Data for Machine-Learnt Climate Model Emulation
R. Parthipan
Damon J. Wischik
42
3
0
08 Oct 2022
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
44
139
0
10 Sep 2019
C-RNN-GAN: Continuous recurrent neural networks with adversarial
  training
C-RNN-GAN: Continuous recurrent neural networks with adversarial training
Olof Mogren
GAN
77
514
0
29 Nov 2016
1