1Blurb::
2Identifier for model block to be used by a method
3
4Description::
5The \c model_pointer is used to specify which \ref model block will be
6used to perform the function evaluations needed by the Dakota method.
7
8<b> Default Behavior </b>
9
10If not specified, a Dakota method will use the last model block
11parsed.  If specified, there must be a \ref model block in the Dakota
12input file that has a corresponding \c id_model with the same name.
13
14<b> Usage Tips </b>
15
16When doing advanced analyses that involve using multiple methods and
17multiple models, defining a \c model_pointer for each method is
18imperative.
19
20See \ref topic-block_pointer for details about pointers.
21
22Topics::	block_pointer
23Examples::
24\verbatim
25environment
26  tabular_data
27  method_pointer = 'UQ'
28
29method
30  id_method = 'UQ'
31  model_pointer = 'SURR'
32  sampling,
33    samples = 10
34    seed = 98765 rng rnum2
35    response_levels = 0.1 0.2 0.6
36                      0.1 0.2 0.6
37                            0.1 0.2 0.6
38    sample_type lhs
39    distribution cumulative
40
41model
42  id_model = 'SURR'
43    surrogate global,
44    dace_method_pointer = 'DACE'
45    polynomial quadratic
46
47method
48  id_method = 'DACE'
49    model_pointer = 'DACE_M'
50    sampling sample_type lhs
51    samples = 121 seed = 5034 rng rnum2
52
53model
54  id_model = 'DACE_M'
55  single
56  interface_pointer = 'I1'
57
58variables
59  uniform_uncertain = 2
60    lower_bounds =  0.   0.
61    upper_bounds =  1.   1.
62    descriptors  = 'x1' 'x2'
63
64interface
65  id_interface = 'I1'
66  system asynch evaluation_concurrency = 5
67    analysis_driver = 'text_book'
68
69responses
70  response_functions = 3
71  no_gradients
72  no_hessians
73\endverbatim
74Theory::
75Faq::
76See_Also::
77