Dakota Reference Manual  Version 6.15
Explore and Predict with Confidence
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mf_pce


Multifidelity Polynomial Chaos Expansion as an emulator model.

Specification

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

Description

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.

See Also

These keywords may also be of interest: