1Blurb:: 2Performs an incremental Latin Hypercube Sampling (LHS) study 3 4Description:: 5Use of \c refinement_samples replaces 6the deprecated \c sample_type \c incremental_lhs and 7\c sample_type \c incremental_random. 8 9An incremental random sampling approach will successively add 10samples to an initial or existing random sampling study 11according to the sequence of \c refinement_samples. Dakota reports 12statistics (mean, variance, percentiles, etc) separately for the 13initial \c samples and for each \c refinement_samples increment at 14the end of the study. For an LHS design, the number of 15\c refinement_samples in each refinement 16level must result in twice the number of previous samples. For 17\c sample_type \c random, there is no constraint on the number of 18samples that can be added. 19 20Often, this approach is used when you have an initial study with 21sample size N1 and you want to perform an additional N1 samples. 22The initial N1 samples may be contained in a Dakota restart file so 23only N1 (instead of 2 x N1) potentially expensive function 24evaluations will be performed. 25 26 27This process can be extended by repeatedly increasing (for LHS: doubling) 28the \c refinement_samples: 29\verbatim 30method 31 sampling 32 seed = 1337 33 samples = 50 34 refinement_samples = 50 100 200 400 800 35\endverbatim 36 37<b> Usage Tips </b> 38 39The incremental approach is useful if it is uncertain how many 40simulations can be completed within available time. 41 42See the examples below and 43the \ref running_dakota-usage and \ref dakota_restart pages. 44 45Topics:: 46Examples:: 47 48Suppose an initial study is conducted using \c sample_type \c lhs 49with \c samples = 50. A follow-on study uses a new input file where 50\c samples = 50, and \c refinement_samples = 50, resulting in 50 51reused samples (from restart) and 50 new random samples. The 50 new 52samples will be combined with the 50 previous samples to generate a 53combined sample of size 100 for the analysis. 54 55One way to ensure the restart file is saved is to specify a non-default name, 56via a command line option: 57\verbatim 58dakota -input LHS_50.in -write_restart LHS_50.rst 59\endverbatim 60 61which uses the input file: 62 63\verbatim 64# LHS_50.in 65 66environment 67 tabular_data 68 tabular_data_file = 'LHS_50.dat' 69 70method 71 sampling 72 seed = 1337 73 sample_type lhs 74 samples = 50 75 76model 77 single 78 79variables 80 uniform_uncertain = 2 81 descriptors = 'input1' 'input2' 82 lower_bounds = -2.0 -2.0 83 upper_bounds = 2.0 2.0 84 85interface 86 analysis_drivers 'text_book' 87 fork 88 89responses 90 response_functions = 1 91 no_gradients 92 no_hessians 93\endverbatim 94and the restart file is written to \c LHS_50.rst. 95 96 97Then an incremental LHS study can be run with: 98\verbatim 99dakota -input LHS_100.in -read_restart LHS_50.rst -write_restart LHS_100.rst 100\endverbatim 101where \c LHS_100.in is shown below, and \c LHS_50.rst is the restart 102file containing the results of the previous LHS study. In the example input 103files for the initial and incremental studies, the values for \c seed match. 104This ensures that the initial 50 samples generated in both runs are the same. 105\verbatim 106# LHS_100.in 107 108environment 109 tabular_data 110 tabular_data_file = 'LHS_100.dat' 111 112method 113 sampling 114 seed = 1337 115 sample_type lhs 116 samples = 50 117 refinement_samples = 50 118 119model 120 single 121 122variables 123 uniform_uncertain = 2 124 descriptors = 'input1' 'input2' 125 lower_bounds = -2.0 -2.0 126 upper_bounds = 2.0 2.0 127 128interface 129 analysis_drivers 'text_book' 130 fork 131 132responses 133 response_functions = 1 134 no_gradients 135 no_hessians 136\endverbatim 137 138The user will get 50 new LHS samples which 139maintain both the correlation and stratification of the original LHS 140sample. The new samples will be combined with the original 141samples to generate a combined sample of size 100. 142 143Theory:: 144Faq:: 145See_Also:: 146