collocation_points
Number of collocation points used to estimate expansion coefficients
Specification
Alias: None
Arguments: INTEGER
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|
Optional (Choose One) |
Regression Algorithm |
Compute the coefficients of a polynomial expansion using least squares |
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Compute the coefficients of a polynomial expansion using orthogonal matching pursuit (OMP) |
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Compute the coefficients of a polynomial expansion by solving the Basis Pursuit \(\ell_1\) -minimization problem using linear programming. |
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Compute the coefficients of a polynomial expansion by solving the Basis Pursuit Denoising \(\ell_1\) -minimization problem using second order cone optimization. |
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Compute the coefficients of a polynomial expansion by using the greedy least angle regression (LAR) method. |
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Compute the coefficients of a polynomial expansion by using the LASSO problem. |
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Optional |
Use cross validation to choose the ‘best’ polynomial order of a polynomial chaos expansion. |
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Optional |
Specify a non-linear the relationship between the expansion order of a polynomial chaos expansion and the number of samples that will be used to compute the PCE coefficients. |
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Optional |
Perform bounds-scaling on response values prior to surrogate emulation |
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Optional |
Use derivative data to construct surrogate models |
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Optional |
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion. |
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Optional |
This describes the behavior of reuse of points in constructing polynomial chaos expansion models. |
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Optional |
Maximum iterations in determining polynomial coefficients |
Description
Specify the number of collocation points used to estimate expansion coefficients using regression approaches.
A corresponding sequence specification is documented at, e.g.,
collocation_points_sequence
and
collocation_points_sequence