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Dakota Reference Manual
Version 6.16
Explore and Predict with Confidence
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Multifidelity Polynomial Chaos Expansion as an emulator model.
Alias: none
Argument(s): none
Child Keywords:
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | p_refinement | Automatic polynomial order refinement | ||
| Optional | max_refinement_iterations | Maximum number of expansion refinement iterations | ||
| Optional | allocation_control | Sample allocation approach for multifidelity expansions | ||
| Optional | discrepancy_emulation | Formulation for emulation of model discrepancies. | ||
| Required (Choose One) | Group 1 | quadrature_order_sequence | Sequence of quadrature orders used in a multi-stage expansion | |
| sparse_grid_level_sequence | Sequence of sparse grid levels used in a multi-stage expansion | |||
| expansion_order_sequence | Sequence of expansion orders used in a multi-stage expansion | |||
| orthogonal_least_interpolation | Build a polynomial chaos expansion from simulation samples using orthogonal least interpolation. | |||
| Optional (Choose One) | Basis Polynomial Family (Group 2) | askey | Select the standardized random variables (and associated basis polynomials) from the Askey family that best match the user-specified random variables. | |
| wiener | Use standard normal random variables (along with Hermite orthogonal basis polynomials) when transforming to a standardized probability space. | |||
| Optional | normalized | The normalized specification requests output of PCE coefficients that correspond to normalized orthogonal basis polynomials | ||
| Optional | export_expansion_file | Export the coefficients and multi-index of a Polynomial Chaos Expansion (PCE) to a file | ||
| Optional (Choose One) | Covariance Type (Group 3) | diagonal_covariance | Display only the diagonal terms of the covariance matrix | |
| full_covariance | Display the full covariance matrix | |||
Selects a multifidelity polynomial chaos expansion (MF PCE) surrogate model to use in the Bayesian likelihood calculations. Most specification options are carried over for using MF PCE as a surrogate within the Bayesian framework.
These keywords may also be of interest: