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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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Aggregation strategy for the QoIs statistics for problems with multiple responses in the MLMC algorithm
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 |
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"
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.