Surrogate modeling

Linear regression

Export a metamodel

Export a metamodel

Create a general linear model metamodel

Create a general linear model metamodel

Create a linear least squares model

Create a linear least squares model

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

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

Get the (output) marginal of a PCE

Get the (output) marginal of a PCE

Compute grouped indices for the Ishigami function

Compute grouped indices for the Ishigami function

Visualize orthonormal polynomials basis

Visualize orthonormal polynomials basis

Validate a polynomial chaos

Validate a polynomial chaos

Create a FCE for dependent inputs: transformation vs domination

Create a FCE for dependent inputs: transformation vs domination

Create a full or sparse polynomial chaos expansion

Create a full or sparse polynomial chaos expansion

Create a PCE by integration on the cantilever beam

Create a PCE by integration on the cantilever beam

Use advanced features for PCE

Use advanced features for PCE

Create a polynomial chaos metamodel from a data set

Create a polynomial chaos metamodel from a data set

Quick start: the Ishigami function

Quick start: the Ishigami function

Plot enumeration function

Plot enumeration function

See pitfalls due to the input distribution

See pitfalls due to the input distribution

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

Create the Conditional expectation of a PCE

Create the Conditional expectation of a PCE

Polynomial chaos expansion cross-validation

Polynomial chaos expansion cross-validation

Gaussian Process Regression

Gaussian process regression: draw the likelihood

Gaussian process regression: draw the likelihood

Gaussian Process Regression: propagate uncertainties

Gaussian Process Regression: propagate uncertainties

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression: multiple input dimensions

Gaussian Process Regression : cantilever beam model

Gaussian Process Regression : cantilever beam model

Multi-output Gaussian Process Regression on the fire satellite model

Multi-output Gaussian Process Regression on the fire satellite model

Gaussian Process Regression: choose an arbitrary trend

Gaussian Process Regression: choose an arbitrary trend

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 : generate trajectories from the metamodel

Gaussian Process Regression : generate trajectories from the metamodel

Gaussian Process Regression: Normalization for optimization

Gaussian Process Regression: Normalization for optimization

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

Gaussian Process Regression: nugget effect and noise

Gaussian Process Regression: nugget effect and noise

Fields surrogate models

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