function_train
UQ method leveraging a functional tensor train surrogate model.
Specification
Alias: None
Arguments: None
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
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Optional |
Automatic polynomial order refinement |
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Optional |
Maximum number of expansion refinement iterations |
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Optional |
Stopping criterion based on objective function or statistics convergence |
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Optional |
define scaling of statistical metrics when adapting UQ surrogates |
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Optional |
Type of solver for forming function train approximations by regression |
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Optional |
Maximum iterations in determining polynomial coefficients |
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Optional |
Maximum number of iterations for cross-approximation during a rank adaptation. |
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Optional |
Convergence tolerance for the optimizer used during the regression solve. |
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Optional |
Perform bounds-scaling on response values prior to surrogate emulation |
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Optional |
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion. |
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Required (Choose One) |
Collocation Control |
Number of collocation points used to estimate expansion coefficients |
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Set the number of points used to build a PCE via regression to be proportional to the number of terms in the expansion. |
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Optional |
An accuracy tolerance that is used to guide rounding during rank adaptation. |
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Optional |
A secondary rounding tolerance used for post-processing |
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Optional |
(Initial) polynomial order of each univariate function within the functional tensor train. |
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Optional |
Activate adaptive procedure for determining the best basis order |
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Optional |
increment used when adapting the basis order in function train methods |
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Optional |
Maximum polynomial order of each univariate function within the functional tensor train. |
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Optional |
Limit the number of cross-validation candidates for basis order |
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Optional |
The initial rank used for the starting point during a rank adaptation. |
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Optional |
Activate adaptive procedure for determining best rank representation |
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Optional |
The increment in rank employed during each iteration of the rank adaptation. |
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Optional |
Limits the maximum rank that is explored during a rank adaptation. |
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Optional |
Limit the number of cross-validation candidates for rank |
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Optional |
Number of samples at which to evaluate an emulator (surrogate) |
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Optional |
Selection of sampling strategy |
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Optional |
Selection of a random number generator |
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Optional |
Allow refinement of probability and generalized reliability results using importance sampling |
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Optional |
Output moments of the specified type and include them within the set of final statistics. |
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Optional |
Values at which to estimate desired statistics for each response |
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Optional |
Specify probability levels at which to estimate the corresponding response value |
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Optional |
Specify reliability levels at which the response values will be estimated |
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Optional |
Specify generalized relability levels at which to estimate the corresponding response value |
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Optional |
Selection of cumulative or complementary cumulative functions |
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Optional |
Activates global sensitivity analysis based on decomposition of response variance into main, interaction, and total effects |
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Optional (Choose One) |
Covariance Type |
Display only the diagonal terms of the covariance matrix |
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Display the full covariance matrix |
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Optional |
Filename for points at which to evaluate the PCE/SC surrogate |
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Optional |
Output file for surrogate model value evaluations |
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Optional |
Seed of the random number generator |
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Optional |
Reuses the same seed value for multiple random sampling sets |
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Optional |
Identifier for model block to be used by a method |
Description
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.
Examples
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