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approximate_control_variate


Approximate control variate (ACV) sampling methods for UQ

Specification

Alias: acv_sampling

Argument(s): none

Child Keywords:

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Required
(Choose One)
Solution Approach (Group 1) acv_independent_sampling

Approximate control variate (ACV) algorithm that employs independent samples per model

acv_multifidelity

Approximate control variate (ACV) algorithm that mimics MFMC by employing a nested pyramid sample pattern

This ACV variant uses sample set definitions that are similar to multifidelity Monte Carlo (MFMC), in that sample sets are nested with each new level adding an increment on top of the previous.

acv_kl

Approximate control variate (ACV) algorithm that optimizes a mix of hierarchial and non-hierarchical schemes

Optional pilot_samples

Initial set of samples for multilevel/multifidelity sampling methods.

Optional solution_mode

Solution mode for multilevel/multifidelity methods

Optional truth_fixed_by_pilot

Option to suppress any increment to the number of truth samples

Optional
(Choose One)
Optimization Solver (Group 2) sqp

Uses a sequential quadratic programming method for underlying optimization

nip

Uses a nonlinear interior point method for underlying optimization

Optional seed_sequence

Sequence of seed values for multi-stage random sampling

Optional fixed_seed

Reuses the same seed value for multiple random sampling sets

Optional sample_type

Selection of sampling strategy

Optional export_sample_sequence

Enable export of multilevel/multifidelity sample sequences to individual files

Optional convergence_tolerance

Stopping criterion based on relative error reduction

Optional max_iterations

Number of iterations allowed for optimizers and adaptive UQ methods

Optional max_function_evaluations

Stopping criterion based on maximum function evaluations

Optional final_moments

Output moments of the specified type and include them within the set of final statistics.

Optional distribution

Selection of cumulative or complementary cumulative functions

Optional rng

Selection of a random number generator

Optional model_pointer

Identifier for model block to be used by a method

Description

An adaptive sampling method that utilizes multifidelity relationships in order to improve efficiency through variance reduction. It employs a non-hierarchical model to manage an unordered set of lower-fidelity approximations to a single truth model.

Compared to multifidelity Monte Carlo (MFMC), ACV relaxes the nested sampling of a recursive emulator, instead targeting the truth model's variance with each control variate pair. While the ensemble of control variates appears identical to MFMC:

\[ \hat{Q}_{HF}^{CV} = \hat{Q}_{HF}^{MC} - \sum_{i=1}^M \beta_i (\hat{Q}_{LF_i}^{MC} - \mathbb{E}[Q_{LF_i}]) \]

the sample patterns used for the constituent estimators differ as depicted in Gorodetsky et al. (2020), Figure 2. Two ACV variants are currently implemented, ACV-MF and ACV-IS, with ACV-KL to follow.

Default Behavior

The approximate_control_variate method employs Monte Carlo sample sets by default, but this default can be overridden to use Latin hypercube sample sets using sample_type lhs.

Expected Output

The approximate_control_variate method reports estimates of the first four moments and a summary of the evaluations performed for each model fidelity and discretization level. The method does not support any level mappings (response, probability, reliability, generalized reliability) at this time.

Expected HDF5 Output

If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5:

In addition, the execution group has the attribute equiv_hf_evals, which records the equivalent number of high-fidelity evaluations.

Usage Tips

The approximate_control_variate method must be used in combination with a non-hierarchical model specification that defines either a model form sequence or a discretization level sequence. For a model form sequence, each model must provide a scalar solution_level_cost. For a discretization level sequence, solution_level_control must identify the variable string descriptor that controls the resolution levels and the associated array of relative costs must be provided using solution_level_cost.

Examples

The following method block:

method,
    model_pointer = 'NONHIER'
    approximate_control_variate             
      acv_mf nip
      pilot_samples = 20 seed = 1237
      max_iterations = 10
      convergence_tolerance = .001

specifies ACV-MF using the nonlinear interior point (NIP) solver in combination with the model identified by the NONHIER pointer.

This NONHIER model specification provides a one-dimensional sequence, here defined by a single truth model and a set of unordered approximation models, each with a single (or default) discretization level:

model,
    id_model = 'NONHIER'
    surrogate non_hierarchical
      truth_model = 'HF'
      unordered_model_fidelities = 'LF1' 'LF2'

model,
    id_model = 'LF1'
    interface_pointer = 'LF1_INT'
    simulation
      solution_level_cost = 1

model,
    id_model = 'LF2'
    interface_pointer = 'LF2_INT'
    simulation
      solution_level_cost = 16

model,
    id_model = 'HF'
    interface_pointer = 'HF_INT'
    simulation
      solution_level_cost = 256.

Refer to dakota/test/dakota_uq_diffusion_acv3_cost4.in and dakota/test/dakota_uq_tunable_acv.in in the source distribution for this case as well as additional examples.

Refer to [Gorodetsky et al., JCP (408), 2020] for more detailed algorithm descriptions, theoretical considerations, and a helpful sample set diagram.

See Also

These keywords may also be of interest: