Comparative analyses of covariance matrix adaptation and iterative ensemble smoother on high-dimensional inverse problems in high-resolution groundwater modeling

•Both CMA-ES and the ES-LM perform well for low dimensionality of unknown parameters.•ES-LM can underestimate uncertainty with small ensemble size.•CMA-ES is more computational demanding than ES-LM for high-dimensionality problems.•ES-LM outperforms the CMA-ES in calibrating high-resolution groundwa...

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Vydané v:	Journal of hydrology (Amsterdam) Ročník 625; s. 130075
Hlavní autori:	Yang, Shuo, Tsai, Frank T.-C., Bacopoulos, Peter, Kees, Christopher E.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.10.2023
Predmet:	Bayesian theory case studies Covariance matrix adaptation groundwater High-dimensional inverse problems High-resolution groundwater model hydrologic models Iterative ensemble smoother Louisiana Mississippi Parallel computing population size system optimization uncertainty variance covariance matrix Louisiana Mississippi Parallel computing High-resolution groundwater model Covariance matrix adaptation High-dimensional inverse problems Iterative ensemble smoother
ISSN:	0022-1694, 1879-2707
On-line prístup:	Získať plný text
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