# DAKOTA INPUT FILE - dakota_uq_textbook_adaptsampling.in
# This sampling input file demonstrates adaptive sampling. 

method,
	adaptive_sampling,
	  initial_samples = 20
	  seed = 1234
	  # rng mt19937
	   
          response_levels =  10.0
          samples_on_emulator = 400
          max_iterations = 50
          refinement_samples = 2
          batch_selection = naive
          fitness_metric = predicted_variance
          #fitness_metric = distance
          #fitness_metric = gradient
          # previous implementation with misc_options: 
          # misc_options  'sample_design=sampling_lhs' 
          # 'approx_type=global_kriging'
          # 'candidate_size=400' 'rounds=50' 'score_type=alm'
          # 'batch_size=2' 'batch_environment=naive'

variables,
	uniform_uncertain = 2
          descriptors  = 'x1' 'x2'
	  lower_bounds =  -2.0   -2.0
	  upper_bounds =  2.0   2.0

interface,
        direct
	  analysis_driver = 'text_book'

responses,
	response_functions = 1
	no_gradients
	no_hessians