def validation_FEM(yname, angles, train_size):
datafem = FEMData(yname, angles)
# datafem = BerkovichData(yname)
if train_size == 80:
kf = RepeatedKFold(n_splits=5, n_repeats=2, random_state=0)
elif train_size == 90:
kf = KFold(n_splits=10, shuffle=True, random_state=0)
else:
kf = ShuffleSplit(n_splits=10,
test_size=len(datafem.X) - train_size,
random_state=0)
mape = []
iter = 0
for train_index, test_index in kf.split(datafem.X):
iter += 1
print("\nCross-validation iteration: {}".format(iter))
X_train, X_test = datafem.X[train_index], datafem.X[test_index]
y_train, y_test = datafem.y[train_index], datafem.y[test_index]
data = dde.data.DataSet(X_train=X_train,
y_train=y_train,
X_test=X_test,
y_test=y_test)
mape.append(dde.apply(nn, (data, )))
print(mape)
print(yname, train_size, np.mean(mape), np.std(mape))
def validation_exp_cross3(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp1 = ExpData("../data/Al6061.csv", yname)
dataexp2 = ExpData("../data/Al7075.csv", yname)
ape = []
y = []
for _ in range(10):
print("\nIteration: {}".format(len(ape)))
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp1.X)),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp1.y)),
X_hi_test=dataexp2.X,
y_hi_test=dataexp2.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt("y.dat", np.hstack(y))
def validation_exp_cross2(yname, train_size):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp1 = ExpData("../data/B3067.csv", yname)
dataexp2 = ExpData("../data/B3090.csv", yname)
ape = []
y = []
kf = ShuffleSplit(n_splits=10, train_size=train_size, random_state=0)
for train_index, _ in kf.split(dataexp1.X):
print("\nIteration: {}".format(len(ape)))
print(train_index)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp1.X[train_index])),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp1.y[train_index])),
X_hi_test=dataexp2.X,
y_hi_test=dataexp2.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname, train_size)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)
def validation_exp_cross(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp = ExpData("../data/B3067.csv", yname)
train_size = 10
ape = []
y = []
# cases = range(6)
# for train_index in itertools.combinations(cases, 3):
# train_index = list(train_index)
# test_index = list(set(cases) - set(train_index))
kf = ShuffleSplit(n_splits=10,
test_size=len(dataexp.X) - train_size,
random_state=0)
for train_index, test_index in kf.split(dataexp.X):
print("\nIteration: {}".format(len(ape)))
print(train_index, "==>", test_index)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=np.vstack((dataBerkovich.X, dataexp.X[train_index])),
y_lo_train=datalow.y,
y_hi_train=np.vstack((dataBerkovich.y, dataexp.y[train_index])),
X_hi_test=dataexp.X[test_index],
y_hi_test=dataexp.y[test_index],
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)
def validation_mf(yname, train_size):
datalow = FEMData(yname, [70])
# datalow = ModelData(yname, 10000, "forward_n")
datahigh = BerkovichData(yname)
# datahigh = FEMData(yname, [70])
kf = ShuffleSplit(n_splits=10,
test_size=len(datahigh.X) - train_size,
random_state=0)
# kf = LeaveOneOut()
mape = []
iter = 0
for train_index, test_index in kf.split(datahigh.X):
iter += 1
print("\nCross-validation iteration: {}".format(iter), flush=True)
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=datahigh.X[train_index],
y_lo_train=datalow.y,
y_hi_train=datahigh.y[train_index],
X_hi_test=datahigh.X[test_index],
y_hi_test=datahigh.y[test_index],
)
mape.append(dde.apply(mfnn, (data, ))[0])
# mape.append(dde.apply(mfgp, (data,)))
print(mape)
print(yname, train_size, np.mean(mape), np.std(mape))
def validation_exp(yname):
datalow = FEMData(yname, [70])
dataBerkovich = BerkovichData(yname)
dataexp = ExpData("../data/B3067.csv", yname)
ape = []
y = []
for iter in range(10):
print("\nIteration: {}".format(iter))
data = dde.data.MfDataSet(
X_lo_train=datalow.X,
X_hi_train=dataBerkovich.X,
y_lo_train=datalow.y,
y_hi_train=dataBerkovich.y,
X_hi_test=dataexp.X,
y_hi_test=dataexp.y,
)
res = dde.apply(mfnn, (data, ))
ape.append(res[:2])
y.append(res[2])
print(yname)
print(np.mean(ape, axis=0), np.std(ape, axis=0))
np.savetxt(yname + ".dat", np.hstack(y).T)
if directory_1.exists() and directory_1.is_dir():
shutil.rmtree(directory_1)
if directory_2.exists() and directory_2.is_dir():
shutil.rmtree(directory_2)
#os.mkdir(directory_1)
os.mkdir(directory_2)
# Network Training
for i in range(len(ds)):
l2_differences_u_0[i], l2_differences_v_0[i], l2_differences_w_0[
i], l2_differences_p_0[i], residuals_0[i], l2_differences_u_1[
i], l2_differences_v_1[i], l2_differences_w_1[
i], l2_differences_p_1[i], residuals_1[i], times[i], epochs[
i] = dde.apply(train_source_model, (ds[i], ))
# Print Information
print("At the beginning: ")
print("Residuals: ", residuals_0)
print("Relative L2 Difference u: ", l2_differences_u_0)
print("Relative L2 Difference v: ", l2_differences_v_0)
print("Relative L2 Difference w: ", l2_differences_w_0)
print("Relative L2 Difference p: ", l2_differences_p_0)
print("\n")
print("In the end: ")
print("Residuals: ", residuals_1)
print("Relative L2 Difference u: ", l2_differences_u_1)
print("Relative L2 Difference v: ", l2_differences_v_1)
print("Relative L2 Difference w: ", l2_differences_w_1)
print("Relative L2 Difference p: ", l2_differences_p_1)
ax = plt.axes(projection="3d")
ax.plot_wireframe(x[:, :, z_index], y[:, :, z_index], p_pred_0[:, :,
z_index])
ax.set_xlabel('location x')
ax.set_ylabel('location y')
ax.set_zlabel('p')
plt.tight_layout()
plt.savefig('Predictions/Predicted_solution_p_d_{}_t_0.png'.format(d),
dpi=300)
plt.figure(7)
ax = plt.axes(projection="3d")
ax.plot_wireframe(x[:, :, z_index], y[:, :, z_index], p_pred_1[:, :,
z_index])
ax.set_xlabel('location x')
ax.set_ylabel('location y')
ax.set_zlabel('p')
plt.tight_layout()
plt.savefig('Predictions/Predicted_solution_p_d_{}_t_1.png'.format(d),
dpi=300)
return
### Main file ###
# Vizualizations
for i in range(len(ds_source)):
dde.apply(visualize_solution, (
ds_source[i],
epochs_source[i],
))
if directory.exists() and directory.is_dir():
shutil.rmtree(directory)
os.mkdir(directory)
# Network Training
for i in range(len(ds_target)):
for j in range(number_of_strategies):
l2_differences_u_0[i, j], l2_differences_v_0[i, j], l2_differences_w_0[
i, j], l2_differences_p_0[i, j], residuals_0[
i, j], l2_differences_u_1[i, j], l2_differences_v_1[
i, j], l2_differences_w_1[i, j], l2_differences_p_1[
i, j], residuals_1[i, j], similarities[i, j], times[
i, j] = dde.apply(train_target_model, (
ds_source,
epochs_source,
ds_target[i],
j,
))
# Print Information
print("At the beginning: ")
print("Residuals: ", residuals_0)
print("Relative L2 Difference u: ", l2_differences_u_0)
print("Relative L2 Difference v: ", l2_differences_v_0)
print("Relative L2 Difference w: ", l2_differences_w_0)
print("Relative L2 Difference p: ", l2_differences_p_0)
print("\n")
print("In the end: ")
print("Residuals: ", residuals_1)
print("Relative L2 Difference u: ", l2_differences_u_1)
print("Relative L2 Difference v: ", l2_differences_v_1)
u_exact = data['usol'].T
x_test, t_test = np.meshgrid(np.linspace(x_min, x_max, test_points_x),
np.linspace(t_min, t_max, test_points_t))
X = np.vstack((np.ravel(x_test), np.ravel(t_test))).T
# Reload model and make predictions
model_name = 'Neural_Networks/nu_{}/Burger_Equation_Source_Model_nu_{}-{}'.format(
nus[i], nus[i], epochs_source[i])
model.compile("adam", lr=learning_rate)
model.train(model_restore_path=model_name, epochs=0)
u_pred = model.predict(X).reshape(test_points_t, test_points_x)
f = model.predict(X, operator=pde)
ax = plt.axes(projection="3d")
ax.plot_wireframe(x_test, t_test, u_pred)
ax.set_xlabel('location x')
ax.set_ylabel('time t')
ax.set_zlabel('u')
plt.tight_layout()
plt.savefig('Predictions/Predicted_solution_nu_{}.png'.format(nus[i]),
dpi=300)
return
### Main file ###
# Vizualizations
for i in range(len(nus)):
dde.apply(visualize_solution, (nus[i], ))
# Foder Structure
directory_1 = Path('Neural_Networks')
directory_2 = Path('Results')
if directory_1.exists() and directory_1.is_dir():
shutil.rmtree(directory_1)
if directory_2.exists() and directory_2.is_dir():
shutil.rmtree(directory_2)
os.mkdir(directory_1)
os.mkdir(directory_2)
# Network Training
for i in range(len(nus_target)):
for j in range(number_of_strategies):
l2_differences[i, j], residuals[i, j], similarities[i, j], times[
i, j] = dde.apply(train_target_model,
(nus_source, epochs_source, nus_target[i], j))
# Print Information
print("Residuals: ", residuals)
print("L2 differences: ", l2_differences)
print("Similarities: ", similarities)
print("Times: ", times)
# Tables
np.savetxt("Results/residuals.csv", residuals, delimiter=",")
np.savetxt("Results/l2_differences.csv", l2_differences, delimiter=",")
np.savetxt("Results/similarities.csv", similarities, delimiter=",")
np.savetxt("Results/times.csv", times, delimiter=",")
directory = Path('Results')
if directory.exists() and directory.is_dir():
shutil.rmtree(directory)
os.mkdir(directory)
# Network Training
for i in range(len(Res_target)):
for j in range(number_of_strategies):
l2_differences_u[i, j], l2_differences_v[i, j], l2_differences_p[
i, j], residuals[i, j], similarities[i,
j], times[i, j] = dde.apply(
train_target_model, (
Res_source,
epochs_source,
Res_target[i],
j,
))
# Print Information
print("Residuals: ", residuals)
print("Relative L2 Difference u: ", l2_differences_u)
print("Relative L2 Difference v: ", l2_differences_v)
print("Relative L2 Difference p: ", l2_differences_p)
print("Similarities: ", similarities)
print("Times: ", times)
# Tables
np.savetxt("Results/residuals.csv", residuals, delimiter=",")
np.savetxt("Results/l2_differences_u.csv", l2_differences_u, delimiter=",")
plt.savefig('Predictions/Predicted_solution_u_Re_{}.png'.format(Re),
dpi=300)
plt.figure(2)
ax = plt.axes(projection="3d")
ax.plot_wireframe(x_test, y_test, v_pred)
ax.set_xlabel('location x')
ax.set_ylabel('location y')
ax.set_zlabel('v')
plt.tight_layout()
plt.savefig('Predictions/Predicted_solution_v_Re_{}.png'.format(Re),
dpi=300)
plt.figure(2)
ax = plt.axes(projection="3d")
ax.plot_wireframe(x_test, y_test, p_pred)
ax.set_xlabel('location x')
ax.set_ylabel('location y')
ax.set_zlabel('p')
plt.tight_layout()
plt.savefig('Predictions/Predicted_solution_p_Re_{}.png'.format(Re),
dpi=300)
return
### Main file ###
# Vizualizations
for i in range(len(Res_source)):
dde.apply(visualize_solution, (Res_source[i], ))
directory_1 = Path('Neural_Networks')
directory_2 = Path('Results')
if directory_1.exists() and directory_1.is_dir():
shutil.rmtree(directory_1)
if directory_2.exists() and directory_2.is_dir():
shutil.rmtree(directory_2)
os.mkdir(directory_1)
os.mkdir(directory_2)
# Network Training
for i in range(len(Res)):
l2_differences_u[i], l2_differences_v[i], l2_differences_p[i], residuals[
i], times[i], epochs[i] = dde.apply(train_source_model, (Res[i], ))
# Print Information
print("Residuals: ", residuals)
print("Relative L2 Difference u: ", l2_differences_u)
print("Relative L2 Difference v: ", l2_differences_v)
print("Relative L2 Difference p: ", l2_differences_p)
print("Times: ", times)
# Tables
np.savetxt("Results/residuals.csv", residuals, delimiter=",")
np.savetxt("Results/l2_differences_u.csv", l2_differences_u, delimiter=",")
np.savetxt("Results/l2_differences_v.csv", l2_differences_v, delimiter=",")
np.savetxt("Results/l2_differences_p.csv", l2_differences_p, delimiter=",")
np.savetxt("Results/times.csv", times, delimiter=",")
np.savetxt("Neural_Networks/epochs.csv", epochs, delimiter=",")
times = np.zeros(len(nus))
epochs = np.zeros(len(nus))
# Folder Structure
directory_1 = Path('Neural_Networks')
directory_2 = Path('Results')
if directory_1.exists() and directory_1.is_dir():
shutil.rmtree(directory_1)
if directory_2.exists() and directory_2.is_dir():
shutil.rmtree(directory_2)
os.mkdir(directory_1)
os.mkdir(directory_2)
# Network Training
for i in range(len(nus)):
l2_differences[i], residuals[i], times[i], epochs[i] = dde.apply(
train_source_model, (nus[i], ))
# Print Information
print("Residuals: ", residuals)
print("L2 differences: ", l2_differences)
print("Times: ", times)
# Tables
np.savetxt("Results/residuals.csv", residuals, delimiter=",")
np.savetxt("Results/l2_differences.csv", l2_differences, delimiter=",")
np.savetxt("Results/times.csv", times, delimiter=",")
np.savetxt("Neural_Networks/epochs.csv", epochs, delimiter=",")
