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.. _Surrogate modeling:
Surrogate modeling
==================
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Linear regression
-----------------
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_export_metamodel_thumb.png
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:doc:`/auto_surrogate_modeling/linear_regression/plot_export_metamodel`
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Export a metamodel
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_create_linear_least_squares_model_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_create_linear_least_squares_model`
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Create a linear least squares model
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_gaussian_process_fitter_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_gaussian_process_fitter`
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Create a general linear model metamodel
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_taylor_approximation_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_taylor_approximation`
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Taylor approximations
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_linear_model_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_linear_model`
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Create a linear model
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_expert_mixture_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_expert_mixture`
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Mixture of experts
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_stepwise_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_stepwise`
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Perform stepwise regression
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_distribution_linear_regression_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_distribution_linear_regression`
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Distribution of estimators in linear regression
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.. image:: /auto_surrogate_modeling/linear_regression/images/thumb/sphx_glr_plot_overfitting_model_selection_thumb.svg
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:doc:`/auto_surrogate_modeling/linear_regression/plot_overfitting_model_selection`
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Over-fitting and model selection
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Polynomial chaos
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_distribution_transformation_thumb.png
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_distribution_transformation`
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Apply a transform or inverse transform on your polynomial chaos
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_build_distribution_thumb.png
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_build_distribution`
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Fit a distribution from an input sample
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_functional_chaos_exploitation_thumb.png
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_functional_chaos_exploitation`
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Polynomial chaos exploitation
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_functional_chaos_getmarginal_thumb.png
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_functional_chaos_getmarginal`
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Get the (output) marginal of a PCE
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_ishigami_grouped_indices_thumb.png
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_ishigami_grouped_indices`
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Compute grouped indices for the Ishigami function
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_functional_chaos_graphs_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_functional_chaos_graphs`
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Polynomial chaos graphs
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_draw_validation_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_draw_validation`
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Validate a polynomial chaos
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_functional_chaos_database_thumb.png
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_functional_chaos_database`
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Create a full or sparse polynomial chaos expansion
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_cantilever_beam_integration_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_cantilever_beam_integration`
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Create a polynomial chaos metamodel by integration on the cantilever beam
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_functional_chaos_advanced_ctors_thumb.png
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_functional_chaos_advanced_ctors`
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Advanced polynomial chaos construction
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_functional_chaos_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_functional_chaos`
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Create a polynomial chaos metamodel from a data set
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_ishigami_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_ishigami`
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Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_enumeratefunction_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_enumeratefunction`
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Plot enumeration rules
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_lognormal_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_lognormal`
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Pitfalls in polynomial chaos expansion due to the input distribution
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_beam_sensitivity_degree_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_beam_sensitivity_degree`
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Polynomial chaos is sensitive to the degree
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_cleaning_strategy_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_cleaning_strategy`
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Create a sparse chaos by integration
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_sobol_confidence_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_sobol_confidence`
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Compute Sobol' indices confidence intervals
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_conditional_expectation_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_conditional_expectation`
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Conditional expectation of a polynomial chaos expansion
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.. image:: /auto_surrogate_modeling/polynomial_chaos/images/thumb/sphx_glr_plot_chaos_cv_thumb.svg
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:doc:`/auto_surrogate_modeling/polynomial_chaos/plot_chaos_cv`
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Polynomial chaos expansion cross-validation
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Gaussian Process Regression
---------------------------
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_likelihood_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_likelihood`
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Gaussian process regression: draw the likelihood
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_propagate_gpr_ishigami_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_propagate_gpr_ishigami`
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Gaussian Process Regression: propagate uncertainties
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr`
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Gaussian Process Regression: multiple input dimensions
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_cantilever_beam_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_cantilever_beam`
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Gaussian Process Regression : cantilever beam model
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_kriging_multioutput_firesatellite_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_kriging_multioutput_firesatellite`
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Multi-output Gaussian Process Regression on the fire satellite model
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_beam_arbitrary_trend_thumb.png
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_beam_arbitrary_trend`
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Gaussian Process Regression: choose an arbitrary trend
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_beam_trend_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_beam_trend`
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Gaussian Process Regression: choose a polynomial trend on the beam model
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_simulate_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_simulate`
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Gaussian Process Regression : generate trajectories from the metamodel
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_normalization_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_normalization`
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Gaussian Process Regression: Normalization for optimization
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_isotropic_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_isotropic`
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Gaussian Process Regression: use an isotropic covariance kernel
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_kriging_branin_function_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_kriging_branin_function`
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Gaussian Process Regression: metamodel of the Branin-Hoo function
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_1d_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_1d`
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Gaussian Process Regression : quick-start
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_kriging_sequential_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_kriging_sequential`
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Sequentially adding new points to a Gaussian Process metamodel
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_hyperparameters_optimization_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_hyperparameters_optimization`
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Gaussian process fitter: configure the optimization solver
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_active_learning_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_active_learning`
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Gaussian Process-based active learning for reliability
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_advanced_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_advanced`
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Advanced Gaussian process regression
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_draw_covariance_models_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_draw_covariance_models`
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Kriging : draw covariance models
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_choose_trend_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_choose_trend`
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Gaussian Process Regression: choose a polynomial trend
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.. image:: /auto_surrogate_modeling/gaussian_process_regression/images/thumb/sphx_glr_plot_gpr_categorical_thumb.svg
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:doc:`/auto_surrogate_modeling/gaussian_process_regression/plot_gpr_categorical`
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Gaussian Process Regression: surrogate model with continuous and categorical variables
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Fields surrogate models
-----------------------
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.. image:: /auto_surrogate_modeling/fields_surrogate_models/images/thumb/sphx_glr_plot_karhunenloeve_validation_thumb.svg
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:doc:`/auto_surrogate_modeling/fields_surrogate_models/plot_karhunenloeve_validation`
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Validation of a Karhunen-Loeve decomposition
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.. image:: /auto_surrogate_modeling/fields_surrogate_models/images/thumb/sphx_glr_plot_viscous_fall_metamodel_thumb.svg
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:doc:`/auto_surrogate_modeling/fields_surrogate_models/plot_viscous_fall_metamodel`
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Viscous free fall: metamodel of a field function
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.. image:: /auto_surrogate_modeling/fields_surrogate_models/images/thumb/sphx_glr_plot_fieldfunction_metamodel_thumb.svg
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:doc:`/auto_surrogate_modeling/fields_surrogate_models/plot_fieldfunction_metamodel`
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Metamodel of a field function
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.. toctree::
:hidden:
:includehidden:
/auto_surrogate_modeling/linear_regression/index.rst
/auto_surrogate_modeling/polynomial_chaos/index.rst
/auto_surrogate_modeling/gaussian_process_regression/index.rst
/auto_surrogate_modeling/fields_surrogate_models/index.rst