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 |
Aggregate the variances over all QoIs to generate a target for each level in a MLMC algorithm. |
|
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.