Meta modeling

General purpose metamodels

Export a metamodel

Export a metamodel

Create a linear least squares model

Create a linear least squares model

Create a general linear model metamodel

Create a general linear model metamodel

Taylor approximations

Taylor approximations

Create a linear model

Create a linear model

Mixture of experts

Mixture of experts

Perform stepwise regression

Perform stepwise regression

Distribution of estimators in linear regression

Distribution of estimators in linear regression

Over-fitting and model selection

Over-fitting and model selection

Polynomial chaos metamodel

Apply a transform or inverse transform on your polynomial chaos

Apply a transform or inverse transform on your polynomial chaos

Fit a distribution from an input sample

Fit a distribution from an input sample

Polynomial chaos exploitation

Polynomial chaos exploitation

Compute grouped indices for the Ishigami function

Compute grouped indices for the Ishigami function

Polynomial chaos graphs

Polynomial chaos graphs

Validate a polynomial chaos

Validate a polynomial chaos

Create a full or sparse polynomial chaos expansion

Create a full or sparse polynomial chaos expansion

Create a polynomial chaos metamodel by integration on the cantilever beam

Create a polynomial chaos metamodel by integration on the cantilever beam

Advanced polynomial chaos construction

Advanced polynomial chaos construction

Create a polynomial chaos metamodel from a data set

Create a polynomial chaos metamodel from a data set

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

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

Plot enumeration rules

Plot enumeration rules

Polynomial chaos is sensitive to the degree

Polynomial chaos is sensitive to the degree

Create a sparse chaos by integration

Create a sparse chaos by integration

Compute Sobol’ indices confidence intervals

Compute Sobol' indices confidence intervals

Conditional expectation of a polynomial chaos expansion

Conditional expectation of a polynomial chaos expansion

Polynomial chaos expansion cross-validation

Polynomial chaos expansion cross-validation

Gaussian Process Regression metamodel

Gaussian process regression: draw the likelihood

Gaussian process regression: draw the likelihood

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression: propagate uncertainties

Gaussian Process Regression: propagate uncertainties

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression: choose an arbitrary trend

Gaussian Process Regression: choose an arbitrary trend

Example of multi output Gaussian Process Regression on the fire satellite model

Example of multi output Gaussian Process Regression on the fire satellite model

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression: choose a polynomial trend on the beam model

Gaussian Process Regression: choose a polynomial trend on the beam model

Gaussian Process Regression: use an isotropic covariance kernel

Gaussian Process Regression: use an isotropic covariance kernel

Gaussian Process Regression: metamodel of the Branin-Hoo function

Gaussian Process Regression: metamodel of the Branin-Hoo function

Gaussian Process Regression : quick-start

Gaussian Process Regression : quick-start

Sequentially adding new points to a Gaussian Process metamodel

Sequentially adding new points to a Gaussian Process metamodel

Gaussian process fitter: configure the optimization solver

Gaussian process fitter: configure the optimization solver

Gaussian Process-based active learning for reliability

Gaussian Process-based active learning for reliability

Advanced Gaussian process regression

Advanced Gaussian process regression

Kriging : draw covariance models

Kriging : draw covariance models

Gaussian Process Regression: choose a polynomial trend

Gaussian Process Regression: choose a polynomial trend

Gaussian Process Regression: surrogate model with continuous and categorical variables

Gaussian Process Regression: surrogate model with continuous and categorical variables

Fields metamodels

Validation of a Karhunen-Loeve decomposition

Validation of a Karhunen-Loeve decomposition

Viscous free fall: metamodel of a field function

Viscous free fall: metamodel of a field function

Metamodel of a field function

Metamodel of a field function