Dakota Reference Manual
Version 6.12
Explore and Predict with Confidence

(Experimental) Adaptively refine a Gaussian process surrogate
This keyword is related to the topics:
Alias: nond_adaptive_sampling
Argument(s): none
Child Keywords:
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Optional  initial_samples  Initial number of samples for samplingbased methods  
Optional  seed  Seed of the random number generator  
Optional  samples_on_emulator  Number of samples at which to evaluate an emulator (surrogate)  
Optional  fitness_metric  (Experimental) Specify the  
Optional  batch_selection  (Experimental) How to select new points  
Optional  refinement_samples  Number of samples used to refine a probabilty estimate or sampling design.  
Optional  import_build_points_file  File containing points you wish to use to build a surrogate  
Optional  export_approx_points_file  Output file for evaluations of a surrogate model  
Optional  misc_options  (Experimental) Hook for algorithmspecific adaptive sampling options  
Optional  max_iterations  Number of iterations allowed for optimizers and adaptive UQ methods  
Optional  response_levels  Values at which to estimate desired statistics for each response  
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  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 
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:
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.
These keywords may also be of interest: