# Meta modeling¶

## General purpose metamodels¶

Create a linear least squares model

Create a general linear model metamodel

Distribution of estimators in linear regression

Over-fitting and model selection

## Polynomial chaos metamodel¶

Apply a transform or inverse transform on your polynomial chaos

Fit a distribution from an input sample

Compute grouped indices for the Ishigami function

Create a full or sparse polynomial chaos expansion

Create a polynomial chaos metamodel by integration on the cantilever beam

Advanced polynomial chaos construction

Create a polynomial chaos metamodel from a data set

Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos

Polynomial chaos expansion cross-validation

Polynomial chaos is sensitive to the degree

Create a sparse chaos by integration

Compute Sobol’ indices confidence intervals

## Kriging metamodel¶

Kriging: propagate uncertainties

Kriging : multiple input dimensions

Kriging : cantilever beam model

Kriging: choose an arbitrary trend

Kriging the cantilever beam model using HMAT

Example of multi output Kriging on the fire satellite model

Kriging : generate trajectories from a metamodel

Kriging: choose a polynomial trend on the beam model

Kriging with an isotropic covariance function

Kriging: metamodel of the Branin-Hoo function

Sequentially adding new points to a kriging

Kriging: configure the optimization solver

Kriging: choose a polynomial trend

Kriging : draw covariance models

Kriging: metamodel with continuous and categorical variables

## Fields metamodels¶

Validation of a Karhunen-Loeve decomposition

Viscous free fall: metamodel of a field function