qoi_aggregation

Aggregation strategy for the QoIs statistics for problems with multiple responses in the MLMC algorithm

Specification

• Alias: None

• Arguments: None

• Default: sum

Child Keywords:

Required/Optional	Description of Group	Dakota Keyword	Dakota Keyword Description
Required (Choose One)	Qoi Aggregation	sum	Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm.
		max	Compute sample allocation for each response and use maximum over responses for each level in a MLMC algorithm

Description

In the multilevel method a variance of the allocation_target is computed for each of the responses and their levels \(Y^i_\ell, i = 1,..., R, \ell = 0,..., L\) . Setting qoi_aggregation describes the rule on how to aggregate those variances over multiple response functions. Supported options are sum (default) and max. For sum, the variances are aggregated and a single sample allocation is computed. For max, an individual sample allocation for each response using the respective variances over levels is computed and the maximum over all responses for each level is taken (worst case scenario allocation).

Default Behavior “sum”

Examples

The following method block

method,
 model_pointer = 'HIERARCH'
        multilevel_sampling
   pilot_samples = 20 seed = 1237
   convergence_tolerance = .01
   allocation_target = mean
      qoi_aggregation = sum

uses the sum rule to aggregate the variance over the qois.