Data underlying the paper: Application of ensemble transform data assimilation methods for parameter estimation in nonlinear problems
doi:10.4121/12987719.v1
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doi: 10.4121/12987719
doi: 10.4121/12987719
Datacite citation style:
Ruchi, S. (Sangeetika); Dubinkina, Svetlana; de Wiljes, Jana (2021): Data underlying the paper: Application of ensemble transform data assimilation methods for parameter estimation in nonlinear problems. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/12987719.v1
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Dataset
This dataset contains data used for the article "Application of ensemble transform data assimilation methods for parameter estimation in nonlinear problems" S.Ruchi, S.Dubinkina, and J. de Wiljes in Nonlin. Processes Geophys. Discuss, 2020, https://doi.org/10.5194/npg-2020-24.
The purpose of this work is to compare ensemble transform data assimilation methods for estimating high-dimensional parameters in a model of groundwater flow with uncertain rock properties.
The purpose of this work is to compare ensemble transform data assimilation methods for estimating high-dimensional parameters in a model of groundwater flow with uncertain rock properties.
history
- 2021-01-11 first online, published, posted
publisher
4TU.ResearchData
associated peer-reviewed publication
Application of ensemble transform data assimilation methods for parameter estimation in nonlinear problems
funding
- Shell-NWO/FOM 14CSER007
- SFB 1294: Data Assimilation – The Seamless Integration of Data and Models (grant code 318763901) [more info...] Deutsche Forschungsgemeinschaft
organizations
Centrum Wiskunde & Informatica, Amsterdam, The Netherlands
DATA
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F2_metrics.m - 2,360 bytesMD5:
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F2_posterior.m - 1,219 bytesMD5:
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KLdivChan.m - 473 bytesMD5:
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NumGrid_F1.m - 469 bytesMD5:
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NumGrid_F2.m - 554 bytesMD5:
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physicalPlot.m - 2,845 bytesMD5:
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plot_boxOnTop.m - 238,246,263 bytesMD5:
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Results_F1.zip - 144,615,222 bytesMD5:
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Truth_F1.m - 193 bytesMD5:
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Truth_F2.m -
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