:orphan: .. _Surrogate modeling: Surrogate modeling ================== .. raw:: html
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Linear regression ----------------- .. raw:: html
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Export a metamodel
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Create a linear least squares model
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Create a general linear model metamodel
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Taylor approximations
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Create a linear model
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Mixture of experts
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Perform stepwise regression
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Distribution of estimators in linear regression
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Over-fitting and model selection
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Polynomial chaos ---------------- .. raw:: html
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Apply a transform or inverse transform on your polynomial chaos
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Fit a distribution from an input sample
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Polynomial chaos exploitation
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Get the (output) marginal of a PCE
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Compute grouped indices for the Ishigami function
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Polynomial chaos graphs
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Validate a polynomial chaos
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Create a full or sparse polynomial chaos expansion
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Create a polynomial chaos metamodel by integration on the cantilever beam
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Advanced polynomial chaos construction
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Create a polynomial chaos metamodel from a data set
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Create a polynomial chaos for the Ishigami function: a quick start guide to polynomial chaos
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Plot enumeration rules
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Pitfalls in polynomial chaos expansion due to the input distribution
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Polynomial chaos is sensitive to the degree
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Create a sparse chaos by integration
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Compute Sobol' indices confidence intervals
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Conditional expectation of a polynomial chaos expansion
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Polynomial chaos expansion cross-validation
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Gaussian Process Regression --------------------------- .. raw:: html
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Gaussian process regression: draw the likelihood
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Gaussian Process Regression: propagate uncertainties
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Gaussian Process Regression: multiple input dimensions
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Gaussian Process Regression : cantilever beam model
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Multi-output Gaussian Process Regression on the fire satellite model
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Gaussian Process Regression: choose an arbitrary trend
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Gaussian Process Regression: choose a polynomial trend on the beam model
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Gaussian Process Regression : generate trajectories from the metamodel
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Gaussian Process Regression: Normalization for optimization
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Gaussian Process Regression: use an isotropic covariance kernel
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Gaussian Process Regression: metamodel of the Branin-Hoo function
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Gaussian Process Regression : quick-start
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Sequentially adding new points to a Gaussian Process metamodel
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Gaussian process fitter: configure the optimization solver
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Gaussian Process-based active learning for reliability
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Advanced Gaussian process regression
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Kriging : draw covariance models
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Gaussian Process Regression: choose a polynomial trend
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Gaussian Process Regression: surrogate model with continuous and categorical variables
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Fields surrogate models ----------------------- .. raw:: html
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Validation of a Karhunen-Loeve decomposition
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Viscous free fall: metamodel of a field function
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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