multifidelity_stoch_collocation

Multifidelity uncertainty quantification using stochastic collocation

Specification

• Alias: None

• Arguments: None

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Optional (Choose One)	Automated Refinement Type	p_refinement	Automatic polynomial order refinement
		h_refinement	Employ h-refinement to refine the grid
Optional		max_refinement_iterations	Maximum number of expansion refinement iterations
Optional		convergence_tolerance	Stopping criterion based on objective function or statistics convergence
Optional		metric_scale	define scaling of statistical metrics when adapting UQ surrogates
Optional		statistics_mode	type of statistical metric roll-up for multifidelity UQ methods
Optional		allocation_control	Sample allocation approach for multifidelity expansions
Optional		discrepancy_emulation	Formulation for emulation of model discrepancies.
Required (Choose One)	Interpolation Grid Type	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
Optional (Choose One)	Basis Polynomial Family	piecewise	Use piecewise local basis functions
		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		use_derivatives	Use derivative data to construct surrogate models
Optional		samples_on_emulator	Number of samples at which to evaluate an emulator (surrogate)
Optional		sample_type	Selection of sampling strategy
Optional		rng	Selection of a random number generator
Optional		probability_refinement	Allow refinement of probability and generalized reliability results using importance sampling
Optional		final_moments	Output moments of the specified type and include them within the set of final statistics.
Optional		response_levels	Values at which to estimate desired statistics for each response
Optional		probability_levels	Specify probability levels at which to estimate the corresponding response value
Optional		reliability_levels	Specify reliability levels at which the response values will be estimated
Optional		gen_reliability_levels	Specify generalized relability levels at which to estimate the corresponding response value
Optional		distribution	Selection of cumulative or complementary cumulative functions
Optional		variance_based_decomp	Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects
Optional (Choose One)	Covariance Type	diagonal_covariance	Display only the diagonal terms of the covariance matrix
		full_covariance	Display the full covariance matrix
Optional		import_approx_points_file	Filename for points at which to evaluate the PCE/SC surrogate
Optional		export_approx_points_file	Output file for surrogate model value evaluations
Optional		seed_sequence	Sequence of seed values for multi-stage random sampling
Optional		fixed_seed	Reuses the same seed value for multiple random sampling sets
Optional		model_pointer	Identifier for model block to be used by a method

Description

As described in method-stoch_collocation, stochastic collocation is a general framework for approximate representation of random response functions in terms of finite-dimensional interpolation bases, using interpolation polynomials that may be either local or global and either value-based or gradient-enhanced.

In the multifidelity case, we decompose this interpolant expansion into several constituent expansions, one per model form or solution control level. In a bi-fidelity case with low-fidelity (LF) and high-fidelity (HF) models and an additive discrepancy approach, we have:

\[R = \sum_{i=1}^{N_p^{LF}} r^{LF}_i L_i(\xi) + \sum_{i=1}^{N_p^{HF}} \delta_i L_i(\xi)\]

where \(\delta_i\) is a coefficient for the discrepancy expansion.

The same specification options are available as described in method-stoch_collocation with one key difference: the coefficient estimation inputs change from a scalar input for a single expansion to a <i>sequence</i> specification for a low-fidelity expansion followed by multiple discrepancy expansions.

To obtain the coefficients \(r_i\) and \(\delta_i\) for each of the expansions, the following options are provided:

1. multidimensional integration by a tensor-product of Gaussian quadrature rules (specified with quadrature_order_sequence, and, optionally, dimension_preference).

2. multidimensional integration by the Smolyak sparse grid method (specified with sparse_grid_level_sequence and, optionally, dimension_preference)

It is important to note that, while quadrature_order_sequence and sparse_grid_level_sequence are array inputs, only one scalar from these arrays is active at a time for a particular expansion estimation. In order to specify anisotropy in resolution across the random variable set, a dimension_preference specification can be used to augment these scalar specifications.

Multifidelity UQ using SC requires that the model selected for iteration by the method specification is a multifidelity surrogate model (see model-surrogate-hierarchical), which defines an ordered_model_sequence (see model-surrogate-hierarchical). Two types of hierarchies are supported: (i) a hierarchy of model forms composed from more than one model within the ordered_model_sequence, or (ii) a hierarchy of discretization levels comprised from a single model within the ordered_model_sequence which in turn specifies a solution_level_control (see model-single-solution_level_control).

In both cases, an expansion will first be formed for the low fidelity model or coarse discretization, using the first value within the coefficient estimation sequence, along with any specified refinement strategy. Second, expansions are formed for one or more model discrepancies (the difference between response results if additive correction or the ratio of results if multiplicative correction), using all subsequent values in the coefficient estimation sequence (if the sequence does not provide a new value, then the previous value is reused) along with any specified refinement strategy. The number of discrepancy expansions is determined by the number of model forms or discretization levels in the hierarchy.

After formation and refinement of the constituent expansions, each of the expansions is combined (added or multiplied) into an expansion that approximates the high fidelity model, from which the final set of statistics are generated.

Additional Resources

Dakota provides access to multifidelity SC methods through the NonDMultilevelStochCollocation class. Refer to the Stochastic Expansion Methods chapter of the Theory Manual [DEG+22] for additional information on the Multifidelity SC algorithm.

Expected HDF5 Output

If Dakota was built with HDF5 support and run with the environment-results_output-hdf5 keyword, this method writes the following results to HDF5:

• hdf5_results-se_moments (expansion moments only)

• hdf5_results-pdf

• hdf5_results-level_mappings

In addition, the execution group has the attribute equiv_hf_evals, which records the equivalent number of high-fidelity evaluations.

Examples

method,
 multifidelity_stoch_collocation
   model_pointer = 'HIERARCH'
   sparse_grid_level_sequence = 4 3 2

model,
 id_model = 'HIERARCH'
 surrogate hierarchical
   ordered_model_fidelities = 'LF' 'MF' 'HF'
   correction additive zeroth_order