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