online_pilot

Specify a solution mode that includes the pilot cost within the sample allocation logic

Specification

  • Alias: None

  • Arguments: None

Child Keywords:

Required/Optional

Description of Group

Dakota Keyword

Dakota Keyword Description

Optional

relaxation

For an online pilot mode, apply under-relaxation to the shared sample increments

Optional

final_statistics

Indicate the type of final statistics to be returned by a UQ method

Description

Multilevel / multifidelity sampling methods are adaptive UQ methods that utilize a pilot sample to estimate an initial set of correlations or variances, and then augment the pilot with additional sample increments to optimally allocate resources. In this mode, the cost of pilot sampling is treated as online cost and is included within the optimal sample allocation logic. It is the only solution mode that is iterative, seeking to converge from the initial pilot to a shared set of samples that is neither over- nor under-estimated. The former over-estimation, while supporting more robust estimation of correlations and variances (refer to the offline_pilot option if a reference solution is needed), is inefficient due to non-optimality of the aggregate sample sets, obscuring the underlying optimal profile. The latter under-estimation results in unnecessary inaccuracy due to reliance on the initial correlation / covariance approximations – if shared sample increments are indicated in the subsequent allocation process, then these should also be used to make better estimates of the shared covariances (through online iteration).

Default Behavior

This iterative online mode (online_pilot) is the default.

Usage Tips

It is typically advantageous to start from a smaller pilot sample and then rely on the iterative updates to increase the shared sample levels to match the optimal sample profile (as dictated by an accuracy target or prescribed budget). Under-relaxation can also be helpful to avoid a shared sample update that is too aggressive based on inaccurate initial information.