1#@ *: Label=FastTest 2#@ *: DakotaConfig=HAVE_QUESO 3#@ *: ReqFiles=dakota_bayes_transforms.withsigma.dat 4# Test scaling/weighting transformations of residuals in Bayesian calibration 5# with mod_cantilever (2D) driver 6# s0-s5: using QUESO 7# s6: using DREAM 8# s0: baseline, no scaling nor weighting transformations 9# s1: scaled 10# s2: weighted 11# s3: scaled and weighted (transformations should cancel each other out) 12# s4: scaled and weighted (transformations compund each other) 13# s5: scaled and weighted (transformations compund each other), using metropolis_hastings 14# s6: scaled and weighted (transformations compund each other), using DREAM 15 16method 17 bayes_calibration queso #s0,#s1,#s2,#s3,#s4,#s5 18# bayes_calibration dream #s6 19 chain_samples = 2000 seed = 1 20 dram #s0,#s1,#s2,#s3,#s4 21# metropolis_hastings #s5 22 proposal_covariance #s0,#s1,#s2,#s3,#s4,#s5 23 values 5.0e8 5.0e-8 #s0,#s3 24# values 5.0e10 5.0e-6 #s1 25# values 5.0e6 5.0e-10 #s2 26# values 5.0e12 5.0e-4 #s4,#s5 27 diagonal #s0,#s1,#s2,#s3,#s4,#s5 28 probability_levels 0.05 0.1 29 0.05 0.1 30 posterior_stats kl_divergence 31# scaling #s1,#s3,#s4,#s5,#s6 32 33variables 34 uniform_uncertain 2 35# MAP estimate is close to 2.9e7 2.5 36 upper_bounds 2.95e7 2.55 37 lower_bounds 2.85e7 2.45 38 initial_point 2.9e7 2.5 39 descriptors 'E' 'w' 40 continuous_state 4 41 initial_state 3 40000 500 1000 42 descriptors 't' 'R' 'X' 'Y' 43 44interface 45 analysis_drivers = 'mod_cantilever' 46 direct 47 48responses 49 calibration_terms = 2 50 calibration_data_file = 'dakota_bayes_transforms.withsigma.dat' 51 freeform 52 num_experiments = 10 53 variance_type = 'scalar' # read 2 scalar sigmas in each row 54 descriptors = 'stress' 'displacement' 55# primary_scales 10.0 10.0 #s1,#s3,#s4,#s5,#s6 56# weights 100.0 100.0 #s2,#s3 57# weights 0.01 0.01 #s4,#s5,#s6 58 no_gradients 59 no_hessians 60