scholarly journals Nested sampling algorithm for subsurface flow model selection, uncertainty quantification, and nonlinear calibration

2013 ◽  
Vol 49 (12) ◽  
pp. 8383-8399 ◽  
Author(s):  
A. H. Elsheikh ◽  
M. F. Wheeler ◽  
I. Hoteit
Keyword(s):  
Model Selection ◽  
Uncertainty Quantification ◽  
Flow Model ◽  
Subsurface Flow ◽  
Sampling Algorithm ◽  
Nested Sampling ◽  
Model Selection Uncertainty ◽  
Nonlinear Calibration

scholarly journals A Nested Sampling Algorithm for Cosmological Model Selection

2006 ◽  
Vol 638 (2) ◽  
pp. L51-L54 ◽  
Author(s):  
Pia Mukherjee ◽  
David Parkinson ◽  
Andrew R. Liddle
Keyword(s):  
Model Selection ◽  
Cosmological Model ◽  
Sampling Algorithm ◽  
Nested Sampling

A scalable coupled surface–subsurface flow model

2015 ◽  
Vol 116 ◽  
pp. 74-87 ◽  
Author(s):  
T. De Maet ◽  
F. Cornaton ◽  
E. Hanert
Keyword(s):  
Flow Model ◽  
Subsurface Flow

scholarly journals Global Sensitivity Analysis and Adaptive Stochastic Sampling of a Subsurface-Flow Model Using Active Subspaces

2019 ◽  
Author(s):  
Daniel Erdal ◽  
Olaf A. Cirpka
Keyword(s):  
Sensitivity Analysis ◽  
Flow Model ◽  
Global Sensitivity Analysis ◽  
Subsurface Flow ◽  
Full Model ◽  
Stochastic Sampling ◽  
Global Sensitivity ◽  
Parameter Combination ◽  
Active Subspaces ◽  
Active Subspace

Abstract. Integrated hydrological modelling of domains with complex subsurface features requires many highly uncertain parameters. Performing a global uncertainty analysis using an ensemble of model runs can help bring clarity which of these parameters really influence system behavior, and for which high parameter uncertainty does not result in similarly high uncertainty of model predictions. However, already creating a sufficiently large ensemble of model simulation for the global sensitivity analysis can be challenging, as many combinations of model parameters can lead to unrealistic model behavior. In this work we use the method of active subspaces to perform a global sensitivity analysis. While building-up the ensemble, we use the already existing ensemble members to construct low-order meta-models based on the first two active subspace dimensions. The meta-models are used to pre-determine whether a random parameter combination in the stochastic sampling is likely to result in unrealistic behavior, so that such a parameter combination is excluded without running the computationally expensive full model. An important reason for choosing the active subspace method is that both the activity score of the global sensitivity analysis and the meta-models can easily be understood and visualized. We test the approach on a subsurface flow model including uncertain hydraulic parameter, uncertain boundary conditions, and uncertain geological structure. We show that sufficiently detailed active subspaces exist for most observations of interest. The pre-selection by the meta-model significantly reduces the number of full model runs that must be rejected due to unrealistic behavior. An essential but difficult part in active subspace sampling using complex models is approximating the gradient of the simulated observation with respect to all parameters. We show that this can effectively and meaningful be done with second-order polynomials.

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