Dakota Reference Manual: qoi_aggregation

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qoi_aggregation

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

## Specification

Alias: none

Argument(s): none

Default: sum

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Required (Choose One) | Qoi Aggregation (Group 1) | 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 . 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.