adaptive_sampling
(Experimental) Adaptively refine a Gaussian process surrogate
Topics
uncertainty_quantification
Specification
Alias: nond_adaptive_sampling
Arguments: None
Child Keywords:
Required/Optional 
Description of Group 
Dakota Keyword 
Dakota Keyword Description 

Optional 
Initial number of samples for samplingbased methods 

Optional 
Seed of the random number generator 

Optional 
Number of samples at which to evaluate an emulator (surrogate) 

Optional 
(Experimental) Specify the


Optional 
(Experimental) How to select new points 

Optional 
Number of samples used to refine a probabilty estimate or sampling design. 

Optional 
File containing points you wish to use to build a surrogate 

Optional 
Output file for surrogate model value evaluations 

Optional 
(Experimental) Hook for algorithmspecific adaptive sampling options 

Optional 
Number of iterations allowed for optimizers and adaptive UQ methods 

Optional 
Values at which to estimate desired statistics for each response 

Optional 
Specify probability levels at which to estimate the corresponding response value 

Optional 
Specify generalized relability levels at which to estimate the corresponding response value 

Optional 
Selection of cumulative or complementary cumulative functions 

Optional 
Selection of a random number generator 

Optional 
Identifier for model block to be used by a method 
Description
This is an experimental capability that is not ready for production use at this point. It was part of an investigation into computational topologybased approaches to feature identification and surrogate refinement.
The goal in performing adaptive sampling is to construct a surrogate model that can be used as an accurate predictor to some expensive simulation, thus it is to one’s advantage to build a surrogate that minimizes the error over the entire domain of interest using as little data as possible from the expensive simulation. The adaptive part alludes to the fact that the surrogate will be refined by focusing samples of the expensive simulation on particular areas of interest rather than rely on random selection or standard spacefilling techniques.
At a highlevel, the adaptive sampling pipeline is a fourstep process:
Evaluate the expensive simulation (referred to as the true model) at initial sample point
Fit a surrogate model
Create a candidate set and score based on information from surrogate
Select a candidate point to evaluate the true model
Loop until done
In terms of the Dakota implementation, the adaptive sampling method currently uses Latin Hypercube sampling (LHS) to generate the initial points in Step 1 above. For Step 2, we use a Gaussian process model.
The default behavior is to add one point at a time. At each iteration (e.g. each loop of Steps 24 above), a Latin Hypercube sample is generated (a new one, different from the initial sample) and the surrogate model is evaluated at this points. These are the candidate points that are then evaluated according to the fitness metric. The number of candidates used in practice should be high enough to fill most of the input domain: we recommend at least hundreds of points for a lowdimensional problem. All of the candidates (samples on the emulator) are given a score and then the highestscoring candidate is selected to be evaluated on the true model.
The adaptive sampling method also can generate batches of points
to add at a time using the batch_selection
and batch_size
keywords.