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Dakota Reference Manual
Version 6.15
Explore and Predict with Confidence
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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 | convergence_tolerance | Stopping criterion based on objective function or statistics convergence | ||
Optional | metric_scale | define scaling of statistical metrics when adapting UQ surrogates | ||
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 cross-approximation during a rank adaptation. | ||
Optional | solver_tolerance | Convergence tolerance for the optimizer used during the regression solve. | ||
Optional | response_scaling | Perform bounds-scaling on response values prior to surrogate emulation | ||
Optional | tensor_grid | Use sub-sampled tensor-product 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 post-processing | ||
Optional | start_order | (Initial) polynomial order of each univariate function within the functional tensor train. | ||
Optional | adapt_order | Activate adaptive procedure for determining the best basis order | ||
Optional | kick_order | increment used when adapting the basis order in function train methods | ||
Optional | max_order | Maximum polynomial order of each univariate function within the functional tensor train. | ||
Optional | max_cv_order_candidates | Limit the number of cross-validation candidates for basis order | ||
Optional | start_rank | The initial rank used for the starting point during a rank adaptation. | ||
Optional | adapt_rank | Activate adaptive procedure for determining best rank representation | ||
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 | max_cv_rank_candidates | Limit the number of cross-validation candidates for rank | ||
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 | import_approx_points_file | Filename for points at which to evaluate the PCE/SC surrogate | ||
Optional | export_approx_points_file | Output file for surrogate model value evaluations | ||
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 input-output mapping. Refer to the function_train surrogate model for additional details.
Usage Tips
This method is a self-contained 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 = 1e-12 rounding_tolerance = 1e-12 convergence_tol = 1e-6 collocation_points = 100 samples_on_emulator = 100000 seed = 531 response_levels = .1 1. 50. 100. 500. 1000. variance_based_decomp
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