def main(parallelEnv):
comm = MPI.COMM_WORLD
myGlobalRank = comm.rank
# Create an Albany problem:
filename = "input_dirichletT.yaml"
parameter = Utils.createParameterList(
filename, parallelEnv
)
problem = Utils.createAlbanyProblem(parameter, parallelEnv)
parameter_map_0 = problem.getParameterMap(0)
parameter_0 = Tpetra.Vector(parameter_map_0, dtype="d")
N = 200
p_min = -2.
p_max = 2.
# Generate N samples randomly chosen in [p_min, p_max]:
p = np.random.uniform(p_min, p_max, N)
QoI = np.zeros((N,))
# Loop over the N samples and evaluate the quantity of interest:
for i in range(0, N):
parameter_0[0] = p[i]
problem.setParameter(0, parameter_0)
problem.performSolve()
response = problem.getResponse(0)
QoI[i] = response.getData()[0]
if myGlobalRank == 0:
if printPlot:
f, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(16,6))
ax1.hist(p)
ax1.set_ylabel('Counts')
ax1.set_xlabel('Random parameter')
ax2.scatter(p, QoI)
ax2.set_ylabel('Quantity of interest')
ax2.set_xlabel('Random parameter')
ax3.hist(QoI)
ax3.set_ylabel('Counts')
ax3.set_xlabel('Quantity of interest')
plt.savefig('UQ.jpeg', dpi=800)
plt.close()
def test_all(self):
cls = self.__class__
rank = cls.comm.getRank()
file_dir = os.path.dirname(__file__)
# Create an Albany problem:
filename = "input_dirichlet_mixed_paramsT.yaml"
parameter = Utils.createParameterList(file_dir + "/" + filename,
cls.parallelEnv)
parameter.sublist("Discretization").set("1D Elements", 10)
parameter.sublist("Discretization").set("2D Elements", 10)
problem = Utils.createAlbanyProblem(parameter, cls.parallelEnv)
parameter_map_0 = problem.getParameterMap(0)
para_0_new = Tpetra.Vector(parameter_map_0, dtype="d")
parameter_map_1 = problem.getParameterMap(1)
para_1_new = Tpetra.Vector(parameter_map_1, dtype="d")
para_1_new[:] = 0.333333
n_values = 5
para_0_values = np.linspace(-1, 1, n_values)
responses = np.zeros((n_values, ))
responses_target = np.array(
[0.69247527, 0.48990929, 0.35681844, 0.29320271, 0.2990621])
tol = 1e-8
for i in range(0, n_values):
para_0_new[:] = para_0_values[i]
problem.setParameter(0, para_0_new)
problem.performSolve()
response = problem.getResponse(0)
responses[i] = response.getData()[0]
print("p = " + str(para_0_values))
print("QoI = " + str(responses))
if rank == 0:
self.assertTrue(
np.abs(np.amax(responses - responses_target)) < tol)
def test_all(self):
debug = True
cls = self.__class__
myGlobalRank = cls.comm.getRank()
nproc = cls.comm.getSize()
# Create an Albany problem:
n_params = 2
filename = "thermal_steady_hessian.yaml"
parameter = Utils.createParameterList(
filename, cls.parallelEnv
)
problem = Utils.createAlbanyProblem(parameter, cls.parallelEnv)
# ----------------------------------------------
#
# 1. Evaluation of the theta star
#
# ----------------------------------------------
l_min = 1.
l_max = 2.
n_l = 4
p = 0.25
l = l_min + np.power(np.linspace(0.0, 1.0, n_l), p) * (l_max-l_min)
theta_star, I_star, F_star, P_star = ee.evaluateThetaStar(l, problem, n_params)
# ----------------------------------------------
#
# 2. Evaluation of the prefactor using SO
#
# ----------------------------------------------
mean = np.array([1., 1.])
cov = np.array([[1., 0.], [0., 1.]])
P_SO = ee.secondOrderEstimator(mean, cov, l, theta_star, I_star, F_star, P_star, problem)
if myGlobalRank == 0:
expected_theta_star = np.loadtxt('expected_theta_star_steady_hessian_'+str(nproc)+'.txt')
expected_I_star = np.loadtxt('expected_I_star_steady_hessian_'+str(nproc)+'.txt')
expected_P_star = np.loadtxt('expected_P_star_steady_hessian_'+str(nproc)+'.txt')
expected_F_star = np.loadtxt('expected_F_star_steady_hessian_'+str(nproc)+'.txt')
expected_P_SO = np.loadtxt('expected_P_steady_hessian_SO_'+str(nproc)+'.txt')
tol = 1e-6
if debug:
for i in range(0, len(expected_theta_star)):
print('i = ' + str(i) + ': theta star: expected value = ' + str(expected_theta_star[i]) + ', computed value = ' + str(theta_star[i]) + ', and diff = ' + str(expected_theta_star[i]-theta_star[i]))
print('i = ' + str(i) + ': I star: expected value = ' + str(expected_I_star[i]) + ', computed value = ' + str(I_star[i]) + ', and diff = ' + str(expected_I_star[i]-I_star[i]))
print('i = ' + str(i) + ': P star: expected value = ' + str(expected_P_star[i]) + ', computed value = ' + str(P_star[i]) + ', and diff = ' + str(expected_P_star[i]-P_star[i]))
print('i = ' + str(i) + ': F star: expected value = ' + str(expected_F_star[i]) + ', computed value = ' + str(F_star[i]) + ', and diff = ' + str(expected_F_star[i]-F_star[i]))
print('i = ' + str(i) + ': P SO: expected value = ' + str(expected_P_SO[i]) + ', computed value = ' + str(P_SO[i]) + ', and diff = ' + str(expected_P_SO[i]-P_SO[i]))
self.assertTrue(np.amax(np.abs(expected_theta_star - theta_star)) < tol)
self.assertTrue(np.amax(np.abs(expected_I_star - I_star)) < tol)
self.assertTrue(np.amax(np.abs(expected_P_star - P_star)) < tol)
self.assertTrue(np.amax(np.abs(expected_F_star - F_star)) < tol)
self.assertTrue(np.amax(np.abs(expected_P_SO - P_SO)) < tol)
def test_all(self):
debug = True
cls = self.__class__
myGlobalRank = cls.comm.getRank()
nproc = cls.comm.getSize()
# Create an Albany problem:
n_params = 2
filename = "thermal_steady.yaml"
parameter = Utils.createParameterList(
filename, cls.parallelEnv
)
problem = Utils.createAlbanyProblem(parameter, cls.parallelEnv)
# ----------------------------------------------
#
# 1. Evaluation of the theta star
#
# ----------------------------------------------
l_min = 0.
l_max = 2.
n_l = 3
l = np.linspace(l_min, l_max, n_l)
theta_star, I_star, F_star, P_star = ee.evaluateThetaStar(l, problem, n_params)
# ----------------------------------------------
#
# 2. Evaluation of the prefactor using IS
#
# ----------------------------------------------
N_samples = 10
mean = np.array([1., 1.])
cov = np.array([[1., 0.], [0., 1.]])
np.random.seed(41)
samples = np.random.multivariate_normal(mean, cov, N_samples)
angle_1 = 0.49999*np.pi
angle_2 = np.pi - angle_1
P_IS = ee.importanceSamplingEstimator(mean, cov, theta_star, F_star, P_star, samples, problem)
P_mixed = ee.mixedImportanceSamplingEstimator(mean, cov, theta_star, F_star, P_star, samples, problem, angle_1, angle_2)
if myGlobalRank == 0:
expected_theta_star = np.loadtxt('expected_theta_star_steady_'+str(nproc)+'.txt')
expected_I_star = np.loadtxt('expected_I_star_steady_'+str(nproc)+'.txt')
expected_P_star = np.loadtxt('expected_P_star_steady_'+str(nproc)+'.txt')
expected_F_star = np.loadtxt('expected_F_star_steady_'+str(nproc)+'.txt')
expected_P_IS = np.loadtxt('expected_P_steady_IS_'+str(nproc)+'.txt')
expected_P_mixed = np.loadtxt('expected_P_steady_mixed_'+str(nproc)+'.txt')
tol = 1e-8
tol_F = 5e-5
if debug:
for i in range(0, len(expected_theta_star)):
print('i = ' + str(i) + ': theta star: expected value = ' + str(expected_theta_star[i]) + ', computed value = ' + str(theta_star[i]) + ', and diff = ' + str(expected_theta_star[i]-theta_star[i]))
print('i = ' + str(i) + ': I star: expected value = ' + str(expected_I_star[i]) + ', computed value = ' + str(I_star[i]) + ', and diff = ' + str(expected_I_star[i]-I_star[i]))
print('i = ' + str(i) + ': P star: expected value = ' + str(expected_P_star[i]) + ', computed value = ' + str(P_star[i]) + ', and diff = ' + str(expected_P_star[i]-P_star[i]))
print('i = ' + str(i) + ': F star: expected value = ' + str(expected_F_star[i]) + ', computed value = ' + str(F_star[i]) + ', and diff = ' + str(expected_F_star[i]-F_star[i]))
print('i = ' + str(i) + ': P IS: expected value = ' + str(expected_P_IS[i]) + ', computed value = ' + str(P_IS[i]) + ', and diff = ' + str(expected_P_IS[i]-P_IS[i]))
print('i = ' + str(i) + ': P mixed: expected value = ' + str(expected_P_mixed[i]) + ', computed value = ' + str(P_mixed[i]) + ', and diff = ' + str(expected_P_mixed[i]-P_mixed[i]))
self.assertTrue(np.amax(np.abs(expected_theta_star - theta_star)) < tol)
self.assertTrue(np.amax(np.abs(expected_I_star - I_star)) < tol)
self.assertTrue(np.amax(np.abs(expected_P_star - P_star)) < tol)
self.assertTrue(np.amax(np.abs(expected_F_star - F_star)) < tol_F)
self.assertTrue(np.amax(np.abs(expected_P_IS - P_IS)) < tol)
self.assertTrue(np.amax(np.abs(expected_P_mixed - P_mixed)) < tol)
def main(parallelEnv):
comm = MPI.COMM_WORLD
myGlobalRank = comm.rank
# Create an Albany problem:
n_params = 2
filename = "thermal_steady_2.yaml"
parameter = Utils.createParameterList(filename, parallelEnv)
problem = Utils.createAlbanyProblem(parameter, parallelEnv)
# ----------------------------------------------
#
# 1. Evaluation of the theta star
#
# ----------------------------------------------
l_min = 8.
l_max = 20.
n_l = 5
p = 1.
l = l_min + np.power(np.linspace(0.0, 1.0, n_l), p) * (l_max - l_min)
theta_star, I_star, F_star, P_star = ee.evaluateThetaStar(
l, problem, n_params)
np.savetxt('theta_star_steady_2.txt', theta_star)
np.savetxt('I_star_steady_2.txt', I_star)
np.savetxt('P_star_steady_2.txt', P_star)
np.savetxt('F_star_steady_2.txt', F_star)
# ----------------------------------------------
#
# 2. Evaluation of the prefactor using IS
#
# ----------------------------------------------
N_samples = 100
mean = np.array([1., 1.])
cov = np.array([[1., 0.], [0., 1.]])
samples = np.random.multivariate_normal(mean, cov, N_samples)
angle_1 = 0.49999 * np.pi
angle_2 = np.pi - angle_1
P_IS = ee.importanceSamplingEstimator(mean, cov, theta_star, F_star,
P_star, samples, problem)
P_mixed = ee.mixedImportanceSamplingEstimator(mean, cov, theta_star,
F_star, P_star, samples,
problem, angle_1, angle_2)
P_SO = ee.secondOrderEstimator(mean, cov, l, theta_star, I_star, F_star,
P_star, problem)
np.savetxt('P_steady_IS_2.txt', P_IS)
np.savetxt('P_steady_mixed_2.txt', P_mixed)
np.savetxt('P_steady_SO_2.txt', P_SO)
problem.reportTimers()
# ----------------------------------------------
#
# 3. Plots
#
# ----------------------------------------------
if n_params == 2:
X = np.arange(1, 7, 0.2)
Y = np.arange(1, 7, 0.25)
Z1, Z2 = evaluate_responses(X, Y, problem, True)
X, Y = np.meshgrid(X, Y)
if myGlobalRank == 0:
if printPlot:
plt.figure()
plt.semilogy(F_star, P_star, 'k*-')
plt.semilogy(F_star, P_IS, 'b*-')
plt.semilogy(F_star, P_mixed, 'r*--')
plt.semilogy(F_star, P_SO, 'g*-')
plt.savefig('extreme_steady_2.jpeg', dpi=800)
plt.close()
if n_params == 2:
plt.figure()
plt.plot(theta_star[:, 0], theta_star[:, 1], '*-')
plt.contour(X, Y, Z1, levels=I_star, colors='g')
plt.contour(X, Y, Z2, levels=F_star, colors='r')
plt.savefig('theta_star_2.jpeg', dpi=800)
plt.close()
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z1)
plt.savefig('Z1_2.jpeg', dpi=800)
plt.close()
fig = plt.figure()
ax = fig.gca(projection='3d')
ax.plot_surface(X, Y, Z2)
plt.savefig('Z2_2.jpeg', dpi=800)
plt.close()
def test_all(self):
cls = self.__class__
rank = cls.comm.getRank()
file_dir = os.path.dirname(__file__)
# Create an Albany problem:
filename = "input_dirichlet_mixed_paramsT.yaml"
parameter = Utils.createParameterList(
file_dir + "/" + filename, cls.parallelEnv
)
parameter.sublist("Discretization").set("1D Elements", 10)
parameter.sublist("Discretization").set("2D Elements", 10)
problem = Utils.createAlbanyProblem(parameter, cls.parallelEnv)
g_target_before = 0.35681844
g_target_after = 0.17388298
g_target_2 = 0.19570272
p_0_target = 0.39886689
p_1_norm_target = 5.37319376038225
tol = 1e-8
problem.performSolve()
response_before_analysis = problem.getResponse(0)
problem.performAnalysis()
para_0 = problem.getParameter(0)
para_1 = problem.getParameter(1)
print(para_0.getData())
print(para_1.getData())
para_1_norm = Utils.norm(para_1.getData(), cls.comm)
print(para_1_norm)
if rank == 0:
self.assertTrue(np.abs(para_0[0] - p_0_target) < tol)
self.assertTrue(np.abs(para_1_norm - p_1_norm_target) < tol)
problem.performSolve()
response_after_analysis = problem.getResponse(0)
print("Response before analysis " + str(response_before_analysis.getData()))
print("Response after analysis " + str(response_after_analysis.getData()))
if rank == 0:
self.assertTrue(np.abs(response_before_analysis[0] - g_target_before) < tol)
self.assertTrue(np.abs(response_after_analysis[0] - g_target_after) < tol)
parameter_map_0 = problem.getParameterMap(0)
para_0_new = Tpetra.Vector(parameter_map_0, dtype="d")
para_0_new[:] = 0.0
problem.setParameter(0, para_0_new)
problem.performSolve()
response = problem.getResponse(0)
print("Response after setParameter " + str(response.getData()))
if rank == 0:
self.assertTrue(np.abs(response[0] - g_target_2) < tol)
