Blurb::
UQ method leveraging a functional tensor train surrogate model.
 
Description::
Tensor train decompositions are approximations that exploit low rank structure
in an input-output mapping.  Refer to the \ref model-surrogate-global-function_train
surrogate model for additional details.
 
<b> Usage Tips </b>
 
This method is a self-contained method alternative to the
\ref model-surrogate-global-function_train surrogate model specification, similar
to current method specifications for polynomial chaos and stochastic collocation.
In particular, this \c function_train method specification directly couples with a
simulation model (optionally identified with a \c model_pointer) and an additional
\c function train surrogate model specification is not required as these options
have been embedded within the method specification.
 
Topics::
 
Examples::
\verbatim
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
\endverbatim
 
See_Also:: model-surrogate-global-function_train, method-polynomial_chaos, method-stoch_collocation