Changeset 212
 Timestamp:
 12/11/10 09:22:20 (5 years ago)
 Location:
 core/trunk/docs
 Files:

 2 edited
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core/trunk/docs/Dev_Main.dox
r169 r212 183 183 Dakota::NonDInterval "NonDInterval" (intervalbased epistemic 184 184 methods). Bayesian calibration methods are prototyped in \ref 185 Dakota::NonDBayesCal "NonDBayesCal".185 Dakota::NonDBayesCalibration "NonDBayesCalibration". At this point, we have an initial implementation of the LANL GPMSA code in \ref Dakota::NonDGPMSABayesCalibration "NonDGPMSABayesianCalibration". 186 186 187 187 <ul> <li> \ref Dakota::NonDSampling "NonDSampling" is further 
core/trunk/docs/Ref_Model.dox
r204 r212 979 979 For the response mappings, the primary and secondary specifications 980 980 provide realvalued multipliers to be applied to subiterator response 981 results. The subiterator response results are defined as follows for981 results. The idea is that the responses from the inner loop are mapped to the outer loop. For example, if the nested model is an uncertainty quantification, aleatory statistics from the inner loop such as the mean inner loop response are mapped to the outer level, where they are treated epistemically, so that intervals on the mean (for example) are calculated. The response mapping defines a vector which multiplies the values from the inner loop to the outer loop. Each row of the mapping corresponds to one outer loop response, where each column of the mapping corresponds to a value from the inner loop. Depending on the number of responses and the particular attributes calculated on the inner loop, there will be a vector of inner loop response values that need to be accounted for in the mapping. The subiterator response results are defined as follows for 982 982 different subiterator types: 983 983 … … 1025 1025 the latter two of which are inserted into the mean distribution 1026 1026 parameters of submodel variables \c 'X' and \c 'Y' (option 1 above). 1027 In this particular example, there are 9 inner loop response attributes and 3 outer loop response functions (one primary response function and 2 secondary functions, such as one objective and two constraints). Each row of the response mapping is a vector which is multiplied (e.g. dotproduct) against the 9 subiterator values to determine the outer loop function. For example, the primary response mapping only picks up the first value from the inner loop. This first value is the mean of the first response function on the inner loop. 1027 1028 The response mappings correspond to 9 subiterator response functions 1028 (e.g., a set of UQ final statistics for 3 response functions, each 1029 with a mean, a standard deviation, and a level mapping). The primary 1029 (e.g., there are 3 response functions, each with a set of UQ final statistics: each has a mean, a standard deviation, and a level mapping in this example. If no probability or reliability levels are specified, the responses would only have a mean and standard deviation). The primary 1030 1030 response mapping maps the first subiterator response function (mean) 1031 1031 into a single objective function, least squares term, or generic … … 1038 1038 standard deviations) into another toplevel nonlinear constraint 1039 1039 (these toplevel nonlinear constraints may be inequality or equality, 1040 as dictated by the toplevel response specification). 1040 as dictated by the toplevel response specification). Note that in many cases, each particular subiterator response will be mapped to a unique outer loop response (for example, in the nested UQ case where one wants to determine an interval on each inner loop statistic). In these cases, the response mapping will be the identity matrix. That is, the primary response mapping will have N rows and N columns corresponding to the N subiterator response values, and the mapping matrix will have a value of one along the diagonal and zeros elsewhere. 1041 1041 1042 1042 Table \ref T6d10 "6.10" provides the specification detail for the model
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