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Multi-fidelity climate model parameterization for better generalization
  and extrapolation

Multi-fidelity climate model parameterization for better generalization and extrapolation

19 September 2023
Mohamed Aziz Bhouri
Liran Peng
Michael S. Pritchard
Pierre Gentine
    AI4CE
ArXivPDFHTML

Papers citing "Multi-fidelity climate model parameterization for better generalization and extrapolation"

5 / 5 papers shown
Title
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial
  Representation Learning
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
Colorado Reed
Ritwik Gupta
Shufan Li
S. Brockman
Christopher Funk
Brian Clipp
Kurt Keutzer
Salvatore Candido
M. Uyttendaele
Trevor Darrell
113
168
0
30 Dec 2022
Bridging observation, theory and numerical simulation of the ocean using
  Machine Learning
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Maike Sonnewald
Redouane Lguensat
Daniel C. Jones
P. Dueben
J. Brajard
Venkatramani Balaji
AI4Cl
AI4CE
38
99
0
26 Apr 2021
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
268
5,652
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
247
9,109
0
06 Jun 2015
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
132
3,263
0
09 Jun 2012
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