Gaussian Process Regression metamodelΒΆ
Gaussian Process Regression: multiple input dimensions
Gaussian Process Regression: propagate uncertainties
Gaussian Process Regression : cantilever beam model

Gaussian Process Regression: choose an arbitrary trend
Example of multi output Gaussian Process Regression on the fire satellite model
Gaussian Process Regression : generate trajectories from the metamodel
Gaussian Process Regression: choose a polynomial trend on the beam model
Gaussian Process Regression: use an isotropic covariance kernel
Gaussian Process Regression: metamodel of the Branin-Hoo function
Sequentially adding new points to a Gaussian Process metamodel

Gaussian process fitter: configure the optimization solver
Gaussian Process-based active learning for reliability
Gaussian Process Regression: choose a polynomial trend
Gaussian Process Regression: surrogate model with continuous and categorical variables