ESM tuning
# Resources
- Better exploration of the parameter space
- #BSC Markus F. and Martí G. work with Genetic algorithms
- MCMC, Nested sampling. Bayesian model selection (see AI/Math and Statistics/Math and Statistics)
- Reinforcement Learning
- #POSTER Machine learning surrogate models for parameter tuning: The Lorenz 96 as a test case (Lguensat)
# References
- #PAPER Bayesian optimization for tuning chaotic systems (Abbas 2014)
- #PAPER Empirical evaluation of Bayesian optimization in parametric tuning of chaotic systems (Abbas 2016)
- #PAPER The Art and Science of Climate Model Tuning (Hourdin 2016)
- #THESIS/PHD
Training methods for climate and neural network models (Abbas 2018)
- Bayesian optimization for tuning chaotic systems, see other two papers Abbas 2014, 2016
- #PAPER How parameter specification of an Earth system model of intermediate complexity influences its climate simulations (Shi 2019)
- #PAPER Model Parameter Optimization: ML-guided trans-resolution tuning of physical models (Partee 2019)
- #PAPER Toward efficient calibration of higher-resolution Earth System Models (Fletcher 2021)