Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Select a surrogate model with global support
Alias: none
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Required (Choose One)  Group 1  gaussian_process  Gaussian Process surrogate model  
mars  Multivariate Adaptive Regression Spline (MARS)  
moving_least_squares  Moving Least Squares surrogate models  
neural_network  Artificial neural network model  
radial_basis  Radial basis function (RBF) model  
polynomial  Polynomial surrogate model  
Optional  domain_decomposition  Piecewise Domain Decomposition for Global Surrogate Models  
Optional (Choose One)  Group 2  total_points  Specified number of training points  
minimum_points  Construct surrogate with minimum number of points  
recommended_points  Construct surrogate with recommended number of points  
Optional (Choose One)  Group 3  dace_method_pointer  Specify a method to gather training data  
actual_model_pointer  A surrogate model pointer that guides a method to whether it should use a surrogate model or compute truth function evaluations  
Optional  reuse_points  Surrogate model training data reuse control  
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  use_derivatives  Use derivative data to construct surrogate models  
Optional  correction  Correction approaches for surrogate models  
Optional  metrics  Compute surrogate quality metrics  
Optional  import_challenge_points_file  Datafile of points to assess surrogate quality 
The global surrogate model requires specification of one of the following approximation types:
All these approximations are implemented in SurfPack[34], except for VPS. In addition, a second version of Gaussian process is implemented directly in Dakota.
Training Data
Training data can be taken from prior runs, stored in a datafile, or by running a Design of Experiments method. The keywords listed below are used to determine how to collect training data:
dace_method_pointer
reuse_points
import_points_file
use_derivatives
The source of training data is determined by the contents of a provided import_points_file
, whether reuse_points
and use_derivatives
are specified, and the contents of the method block specified by dace_method_pointer
. use_derivatives
is a special case, the other keywords are discussed below.The number of training data points used in building a global approximation is determined by specifying one of three point counts:
minimum_points:
minimum required or minimum "reasonable" amount of training data. Defaults to d+1 for d input dimensions for most models, e.g., polynomials override to the number of coefficients required to estimate the requested order.
recommended_points:
recommended number of training data, (this is the default option, if none of the keywords is specified). Defaults to 5*d, except for polynomials where it's equal to the minimum.
total_points:
specify the number of training data points. However, if the total_points
value is less than the default minimum_points
value, the minimum_points
value is used. The sources of training data depend on the number of training points, , the number of points in the import file, , and the value of reuse_points
.
reuse_points
is: none
 all points from DACE method region
 only the points within a trust region are taken from the import file, and all remaining points are from the DACE method. all
 (Default) all points from the file are used, and the remaining points come from the DACE method Surrogate Correction
A correction
model can be added to the constructed surrogate in order to better match the training data. The specified correction method will be applied to the surrogate, and then the corrected surrogate model is used by the method.
Finally, the quality of the surrogate can be tested using the metrics
and challenge_points_file
keywords.
Global methods, also referred to as response surface methods, involve many points spread over the parameter ranges of interest. These surface fitting methods work in conjunction with the sampling methods and design of experiments methods.
Procedures for Surface Fitting
The surface fitting process consists of three steps:
In cases where there is more than one response quantity (e.g., an objective function plus one or more constraints), then a separate surface is built for each response quantity. Currently, the surface fit models are built using only 0 order information (function values only), although extensions to using higherorder information (gradients and Hessians) are possible.
Each surface fitting method employs a different numerical method for computing its internal coefficients. For example, the polynomial surface uses a leastsquares approach that employs a singular value decomposition to compute the polynomial coefficients, whereas the kriging surface uses Maximum Likelihood Estimation to compute its correlation coefficients. More information on the numerical methods used in the surface fitting codes is provided in the Dakota Developers Manual.
These keywords may also be of interest: