# function_train

Global surrogate model based on functional tensor train decomposition

**Specification**

*Alias:*None*Arguments:*None

**Child Keywords:**

Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
---|---|---|---|

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 |
||

Optional |
Use sub-sampled tensor-product quadrature points to build a polynomial chaos expansion. |
||

Optional |
An accuracy tolerance that is used to guide rounding during rank adaptation. |
||

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 |

**Description**

Tensor train decompositions are approximations that exploit low rank structure in an input-output mapping. The form of the approximation can be written as a set of matrix valued products:

where the “cores” expand to

An example expansion over four random variables with rank vector (1,7,5,3,1) is

In the current implementation, orthogonal polynomial basis functions (Hermite and Legendre) are employed as the basis functions \(f_i^{jk}(x_i)\) , although the C3 library will enable additional options in the future.

The number of coefficients that must be computed by the regression solver can be inferred from the construction above. For each QoI, the regression size can be determined as follows:

For a v variables, orders a o is a v-vector and ranks a r is a v+1-vector

the first core is a \(1 \times r_1\) row vector and contributes \((o_0 + 1) r_1\) terms

the last core is a \(r_{v-1} \times 1\) col vector and contributes \((o_{v-1}+1) r_{v-1}\) terms

the middle v-2 cores are \(r_i \times r_{i+1}\) matrices that contribute \(r_i r_{i+1} (o_i + 1)\) terms, \(i = 1, ..., v-2\)

neighboring vec/mat dimensions must match, so there are v-1 unique ranks

*Usage Tips*

This new capability is stabilizing and beginning to be embedded in higher-level strategies such as multilevel-multifidelity algorithms. It is not included in the Dakota build by default, as some C3 library dependencies (CBLAS) can induce small differences in our regression suite.

This capability is also being used as a prototype to explore
model-based versus method-based specification of stochastic
expansions. While the model specification is stand-alone, it
currently requires a corresponding method specification to exercise
the model, which can be a generic UQ strategy such as
`surrogate_based_uq`

method or a `sampling`

method. The intent is to
migrate function train, polynomial chaos, and stochastic collocation
toward model-only specifications that can then be employed in any
surrogate/emulator context.

**Examples**

```
model,
id_model = 'FT'
surrogate global function_train
start_order = 2
start_rank = 2 kick_rank = 2 max_rank = 10
adapt_rank
dace_method_pointer = 'SAMPLING'
```