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