Dakota Reference Manual  Version 6.1
Large-Scale Engineering Optimization and Uncertainty Analysis
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sampling


Randomly samples variables according to their distributions

Topics

This keyword is related to the topics:

Specification

Alias: nond_sampling

Argument(s): none

Required/Optional Description of Group Dakota Keyword Dakota Keyword Description
Optional sample_type

Selection of sampling strategy

Optional variance_based_decomp

Activates global sensitivity analysis based on decomposition of response variance into contributions from variables

Optional backfill

Ensures that the samples of discrete variables with finite support are unique

Optional fixed_seed

Reuses the same seed value for multiple random sampling sets

Optional reliability_levels Specify reliability levels at which the response values will be estimated
Optional response_levels

Values at which to estimate desired statistics for each response

Optional distribution

Selection of cumulative or complementary cumulative functions

Optional probability_levels Specify probability levels at which to estimate the corresponding response value
Optional gen_reliability_levels Specify generalized relability levels at which to estimate the corresponding response value
Optional rng

Selection of a random number generator

Optional samples

Number of samples for sampling-based methods

Optional seed

Seed of the random number generator

Optional model_pointer

Identifier for model block to be used by a method

Description

This method generates parameter values by drawing samples from the specified uncertain variable probability distributions. The computational model is executed over all generated parameter values to compute the responses for which statistics are computed. The statistics support sensitivity analysis and uncertainty quantification.

Default Behavior

By default, sampling methods operate on aleatory and epistemic uncertain variables. The types of variables can be restricted or expanded (to include design or state variables) through use of the active keyword in the variables block in the Dakota input file. If continuous design and/or state variables are designated as active, the sampling algorithm will treat them as parameters with uniform probability distributions between their upper and lower bounds.

The following keywords change how the samples are selected:

  • sample_type
  • fixed_seed
  • rng
  • samples
  • seed
  • variance_based_decomp

Expected Outputs

As a default, Dakota provides correlation analyses when running LHS. Correlation tables are printed with the simple, partial, and rank correlations between inputs and outputs. These can be useful to get a quick sense of how correlated the inputs are to each other, and how correlated various outputs are to inputs. variance_based_decomp is used to request more sensitivity information, with additional cost.

Additional statistics can be computed from the samples using the following keywords:

  • response_levels
  • reliability_levels
  • probability_levels
  • gen_reliability_levels

response_levels computes statistics at the specified response value. The other three allow the specification of the statistic value, and will estimate the corresponding response value.

distribution is used to specify whether the statistic values are from cumulative or complementary cumulative functions.

Usage Tips

sampling is a robust approach to doing sensitivity analysis and uncertainty quantification that can be applied to any problem. It requires more simulations than newer, advanced methods. Thus, an alternative may be preferable if the simulation is computationally expensive.

Examples

# tested on Dakota 6.0 on 140501

environment
  tabular_data
    tabular_data_file = 'Sampling_basic.dat'

method
  sampling
    sample_type lhs
    samples = 20

model
  single

variables
  active uncertain
  uniform_uncertain = 2
    descriptors  =   'input1'     'input2'
    lower_bounds =  -2.0     -2.0
    upper_bounds =   2.0      2.0
  continuous_state = 1
    descriptors =   'constant1'
    initial_state = 100

interface
  analysis_drivers 'text_book'
    fork

responses
  response_functions = 1
  no_gradients
  no_hessians

This example illustrates a basic sampling Dakota input file.

  • LHS is used instead of purely random sampling.
  • The default random number generator is used.
  • Without a seed specified, this will not be reproducable
  • In the variables block, two types of variables are used
  • Only the uncertain variables are varied, this is the default behavior, and is also specified by the active keyword, w/ the uncertain option

See Also

These keywords may also be of interest:

FAQ

Q: Do I need to keep the LHS* and S4 files? A: No