Dakota Reference Manual
Version 6.12
Explore and Predict with Confidence

UQ method leveraging a functional tensor train surrogate model.
Alias: none
Argument(s): none
Child Keywords:
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Optional  p_refinement  Automatic polynomial order refinement  
Optional  max_refinement_iterations  Maximum number of expansion refinement iterations  
Optional  regression_type  Type of solver for forming function train approximations by regression  
Optional  max_solver_iterations  Maximum iterations in determining polynomial coefficients  
Optional  max_cross_iterations  Maximum number of iterations for crossapproximation during a rank adaptation.  
Optional  solver_tolerance  Convergence tolerance for the optimizer used during the regression solve.  
Optional  tensor_grid  Use subsampled tensorproduct quadrature points to build a polynomial chaos expansion.  
Required (Choose One)  Collocation Control (Group 1)  collocation_points  Number of collocation points used to estimate expansion coefficients  
collocation_ratio  Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion.  
Optional  rounding_tolerance  An accuracy tolerance that is used to guide rounding during rank adaptation.  
Optional  arithmetic_tolerance  A secondary rounding tolerance used for postprocessing  
Optional  start_order  (Initial) polynomial order of each univariate function within the functional tensor train.  
Optional  max_order  Maximum polynomial order of each univariate function within the functional tensor train.  
Optional  start_rank  The initial rank used for the starting point during a rank adaptation.  
Optional  kick_rank  The increment in rank employed during each iteration of the rank adaptation.  
Optional  max_rank  Limits the maximum rank that is explored during a rank adaptation.  
Optional  adapt_rank  Activate adaptive procedure for determining best rank representation  
Optional  samples_on_emulator  Number of samples at which to evaluate an emulator (surrogate)  
Optional  sample_type  Selection of sampling strategy  
Optional  rng  Selection of a random number generator  
Optional  probability_refinement  Allow refinement of probability and generalized reliability results using importance sampling  
Optional  final_moments  Output moments of the specified type and include them within the set of final statistics.  
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  reliability_levels  Specify reliability levels at which the response values will be estimated  
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  variance_based_decomp  Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects  
Optional (Choose One)  Covariance Type (Group 2)  diagonal_covariance  Display only the diagonal terms of the covariance matrix  
full_covariance  Display the full covariance matrix  
Optional  convergence_tolerance  Stopping criterion based on objective function or statistics convergence  
Optional  import_approx_points_file  Filename for points at which to evaluate the PCE/SC surrogate  
Optional  export_approx_points_file  Output file for evaluations of a surrogate model  
Optional  seed  Seed of the random number generator  
Optional  fixed_seed  Reuses the same seed value for multiple random sampling sets  
Optional  model_pointer  Identifier for model block to be used by a method 
Tensor train decompositions are approximations that exploit low rank structure in an inputoutput mapping. Refer to the function_train surrogate model for additional details.
Usage Tips
This method is a selfcontained method alternative to the function_train surrogate model specification, similar to current method specifications for polynomial chaos and stochastic collocation. In particular, this function_train
method specification directly couples with a simulation model (optionally identified with a model_pointer
) and an additional function
train surrogate model specification is not required as these options have been embedded within the method specification.
method, function_train start_order = 2 start_rank = 2 kick_rank = 2 max_rank = 10 adapt_rank solver_tolerance = 1e12 rounding_tolerance = 1e12 convergence_tol = 1e6 collocation_points = 100 samples_on_emulator = 100000 seed = 531 response_levels = .1 1. 50. 100. 500. 1000. variance_based_decomp
These keywords may also be of interest: