def test_scalarQoI(self):
systemsize = 3
mu = np.random.rand(systemsize)
std_dev = np.diag(np.random.rand(systemsize))
jdist = cp.MvNormal(mu, std_dev)
# Create QoI Object
QoI = Paraboloid3D(systemsize)
# Create the Monte Carlo object
QoI_dict = {
'paraboloid': {
'QoI_func': QoI.eval_QoI,
'output_dimensions': 1,
}
}
nsample = 1000000
mc_obj = MonteCarlo(nsample, jdist, QoI_dict)
mc_obj.getSamples(jdist)
# Get the mean and variance using Monte Carlo
mu_js = mc_obj.mean(jdist, of=['paraboloid'])
var_js = mc_obj.variance(jdist, of=['paraboloid'])
# Analytical mean
mu_j_analytical = QoI.eval_QoI_analyticalmean(mu, cp.Cov(jdist))
err = abs((mu_js['paraboloid'] - mu_j_analytical) / mu_j_analytical)
self.assertTrue(err < 1e-3)
# Analytical variance
var_j_analytical = QoI.eval_QoI_analyticalvariance(mu, cp.Cov(jdist))
err = abs((var_js['paraboloid'] - var_j_analytical) / var_j_analytical)
# print('var_js paraboloid = ', var_js['paraboloid'], '\n')
self.assertTrue(err < 1e-2)
reduced_collocation=True,
include_derivs=False,
dominant_dir=dominant_dir)
mc_obj.getSamples(jdist, include_derivs=False)
else:
print('Using Full Monte Carlo')
nsample = 10000 # 5000
mc_obj = MonteCarlo(nsample,
jdist,
QoI_dict,
reduced_collocation=False,
include_derivs=False)
mc_obj.getSamples(jdist, include_derivs=False)
mu_j = mc_obj.mean(jdist, of=['time_duration'])
var_j = mc_obj.variance(jdist, of=['time_duration'])
# Print everything
print()
if use_reduced_collocation:
print("Using Monte Carlo method along dominant directions.")
else:
print('Using Full Monte Carlo.')
print('mean time duration = ', mu_j['time_duration'])
print('variance time_duration = ', var_j['time_duration'])
print()
statistics_time = time.time() - (start_time + initialization_time)
# Get the statistics
schedule_dict = QoI.compute_schedule_statistics()
QoI_dict = {'fuelburn' : {'QoI_func' : QoI.eval_QoI,
'output_dimensions' : 1
},
}
# Create the Monte Carlo object
start_time1 = time.time()
nsample = int(sys.argv[1])
mc_obj = MonteCarlo(nsample, jdist, QoI_dict) # tjdist: truncated normal distribution
mc_obj.getSamples(jdist) #
t1 = time.time()
# Compute the statistical moments using Monte Carlo
mu_j_mc = mc_obj.mean(jdist, of=['fuelburn'])
t2 = time.time()
var_j_mc = mc_obj.variance(jdist, of=['fuelburn'])
t3 = time.time()
print('Monte Carlo samples = ', nsample)
print("mean_mc = ", mu_j_mc['fuelburn'][0])
print("var_mc = ", var_j_mc['fuelburn'][0])
print()
# mean_sc_fuelburn = 5.269295151614887
# var_sc_fuelburn = 0.34932256
# err_mu = abs((mean_sc_fuelburn - mu_j_mc['fuelburn']) / mean_sc_fuelburn)
# err_var = abs((var_sc_fuelburn - var_j_mc['fuelburn']) / var_sc_fuelburn)
# print("err mu = ", err_mu)
# print("err var = ", err_var)
print('\n-------- Timing Results -------\n')
prep_time_mc = t1 - start_time1
elapsed_time = end_time - start_time
print()
print('time elapsed = ', elapsed_time)
else:
raise NotImplementedError
print()
print(sol)
print(sol.fStar)
print()
print("twist = ", UQObj.QoI.p['oas_scaneagle.wing.geometry.twist'])
print("thickness =", UQObj.QoI.p['oas_scaneagle.wing.thickness'])
print('twist_cp = ', UQObj.QoI.p['oas_scaneagle.wing.twist_cp'])
print('thickness_cp = ', UQObj.QoI.p['oas_scaneagle.wing.thickness_cp'])
print("sweep = ", UQObj.QoI.p['oas_scaneagle.wing.sweep'])
print("aoa = ", UQObj.QoI.p['oas_scaneagle.alpha'])
print()
print('time elapsed = ', elapsed_time)
# Compute the statistical moments
mc_obj.getSamples(UQObj.jdist)
mu_j = mc_obj.mean(UQObj.jdist, of=['fuelburn', 'constraints'])
var_j = mc_obj.variance(UQObj.jdist, of=['fuelburn', 'con_failure'])
print('mu fuelburn = ', mu_j['fuelburn'])
print('var fuelburn = ', var_j['fuelburn'])
print('mu_j KS = ', mu_j['constraints'][0])
print('var_j KS = ', var_j['con_failure'][0])
print('robust KS = ', mu_j['constraints'][0] + 2 * np.sqrt(var_j['con_failure'][0]))
print('mu_j_lift = ', mu_j['constraints'][n_thickness_intersects+1])
print('mu_j CM = ', mu_j['constraints'][-2])