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Example: model comparison
*************************
This example shows the multi-model comparison.
You will see how to perform a multi-model comparison
Provided are three models, a linear models with 2 input parameters, a nonlinear model with 2 input parameters
and a nonlinear model with 4 input parameters.
The data to base the comparison on is given in an extra file.
.. note::
A detailed explanation of this example will be provided in future as part of the tutorial.
Model 1: L2_model
=================
.. list-table:: Pylink model1
:widths: 30 30
:header-rows: 1
* - Property
- Setting
* - Model type
- Function (linear)
* - Number of input parameters
- 2
* - Number of output parameters
- 1
* - Time- or space- dependency
- space-dependency
* - MC reference
- No
.. list-table:: Priors1
:widths: 30 30
:header-rows: 1
* - Parameter
- Distribution
* - 0-2
- given as correlated samples
Model 1: NL2_model
==================
.. list-table:: Pylink model1
:widths: 30 30
:header-rows: 1
* - Property
- Setting
* - Model type
- Function (exponential)
* - Number of input parameters
- 2
* - Number of output parameters
- 1
* - Time- or space- dependency
- space-dependency
* - MC reference
- No
.. list-table:: Priors1
:widths: 30 30
:header-rows: 1
* - Parameter
- Distribution
* - 0-2
- given as correlated samples
Model 1: NL4_model
==================
.. list-table:: Pylink model1
:widths: 30 30
:header-rows: 1
* - Property
- Setting
* - Model type
- Function (cosine)
* - Number of input parameters
- 4
* - Number of output parameters
- 1
* - Time- or space- dependency
- space-dependency
* - MC reference
- No
.. list-table:: Priors1
:widths: 30 30
:header-rows: 1
* - Parameter
- Distribution
* - 0-4
- given as correlated samples
Surrogates 1-3
==============
All surrogates share the same setup and only differ in the given model.
.. list-table:: MetaModel settings
:widths: 30 30
:header-rows: 1
* - Property
- Setting
* - surrogate-type
- aPCE
* - associated model
- see lists above
* - degree choices
- 1-12, q-norm truncation 1.0
* - regression
- OMP (Orthogonal matching pursuit)
.. list-table:: Training choices
:widths: 30 30
:header-rows: 1
* - Property
- Setting
* - Static sampling method
- latin-hypercube
* - Number of static samples
- 100
* - Number of total samples
- 100