Boeing 777 engine data set¶
import numpy as np
import os
def get_b777_engine():
this_dir = os.path.split(__file__)[0]
nt = 12 * 11 * 8
xt = np.loadtxt(os.path.join(this_dir, "b777_engine_inputs.dat")).reshape((nt, 3))
yt = np.loadtxt(os.path.join(this_dir, "b777_engine_outputs.dat")).reshape((nt, 2))
dyt_dxt = np.loadtxt(os.path.join(this_dir, "b777_engine_derivs.dat")).reshape(
(nt, 2, 3)
)
xlimits = np.array([[0, 0.9], [0, 15], [0, 1.0]])
return xt, yt, dyt_dxt, xlimits
def plot_b777_engine(xt, yt, limits, interp):
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
val_M = np.array(
[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9]
) # 12
val_h = np.array(
[0.0, 0.6096, 1.524, 3.048, 4.572, 6.096, 7.62, 9.144, 10.668, 11.8872, 13.1064]
) # 11
val_t = np.array([0.05, 0.2, 0.3, 0.4, 0.6, 0.8, 0.9, 1.0]) # 8
def get_pts(xt, yt, iy, ind_M=None, ind_h=None, ind_t=None):
eps = 1e-5
if ind_M is not None:
M = val_M[ind_M]
keep = abs(xt[:, 0] - M) < eps
xt = xt[keep, :]
yt = yt[keep, :]
if ind_h is not None:
h = val_h[ind_h]
keep = abs(xt[:, 1] - h) < eps
xt = xt[keep, :]
yt = yt[keep, :]
if ind_t is not None:
t = val_t[ind_t]
keep = abs(xt[:, 2] - t) < eps
xt = xt[keep, :]
yt = yt[keep, :]
if ind_M is None:
data = xt[:, 0], yt[:, iy]
elif ind_h is None:
data = xt[:, 1], yt[:, iy]
elif ind_t is None:
data = xt[:, 2], yt[:, iy]
if iy == 0:
data = data[0], data[1] / 1e6
elif iy == 1:
data = data[0], data[1] / 1e-4
return data
num = 100
x = np.zeros((num, 3))
lins_M = np.linspace(0.0, 0.9, num)
lins_h = np.linspace(0.0, 13.1064, num)
lins_t = np.linspace(0.05, 1.0, num)
def get_x(ind_M=None, ind_h=None, ind_t=None):
x = np.zeros((num, 3))
x[:, 0] = lins_M
x[:, 1] = lins_h
x[:, 2] = lins_t
if ind_M:
x[:, 0] = val_M[ind_M]
if ind_h:
x[:, 1] = val_h[ind_h]
if ind_t:
x[:, 2] = val_t[ind_t]
return x
nrow = 6
ncol = 2
ind_M_1 = -2
ind_M_2 = -5
ind_t_1 = 1
ind_t_2 = -1
plt.close()
# --------------------
fig, axs = plt.subplots(6, 2, gridspec_kw={"hspace": 0.5}, figsize=(15, 25))
axs[0, 0].set_title("M={}".format(val_M[ind_M_1]))
axs[0, 0].set(xlabel="throttle", ylabel="thrust (x 1e6 N)")
axs[0, 1].set_title("M={}".format(val_M[ind_M_1]))
axs[0, 1].set(xlabel="throttle", ylabel="SFC (x 1e-3 N/N/s)")
axs[1, 0].set_title("M={}".format(val_M[ind_M_2]))
axs[1, 0].set(xlabel="throttle", ylabel="thrust (x 1e6 N)")
axs[1, 1].set_title("M={}".format(val_M[ind_M_2]))
axs[1, 1].set(xlabel="throttle", ylabel="SFC (x 1e-3 N/N/s)")
# --------------------
axs[2, 0].set_title("throttle={}".format(val_t[ind_t_1]))
axs[2, 0].set(xlabel="altitude (km)", ylabel="thrust (x 1e6 N)")
axs[2, 1].set_title("throttle={}".format(val_t[ind_t_1]))
axs[2, 1].set(xlabel="altitude (km)", ylabel="SFC (x 1e-3 N/N/s)")
axs[3, 0].set_title("throttle={}".format(val_t[ind_t_2]))
axs[3, 0].set(xlabel="altitude (km)", ylabel="thrust (x 1e6 N)")
axs[3, 1].set_title("throttle={}".format(val_t[ind_t_2]))
axs[3, 1].set(xlabel="altitude (km)", ylabel="SFC (x 1e-3 N/N/s)")
# --------------------
axs[4, 0].set_title("throttle={}".format(val_t[ind_t_1]))
axs[4, 0].set(xlabel="Mach number", ylabel="thrust (x 1e6 N)")
axs[4, 1].set_title("throttle={}".format(val_t[ind_t_1]))
axs[4, 1].set(xlabel="Mach number", ylabel="SFC (x 1e-3 N/N/s)")
axs[5, 0].set_title("throttle={}".format(val_t[ind_t_2]))
axs[5, 0].set(xlabel="Mach number", ylabel="thrust (x 1e6 N)")
axs[5, 1].set_title("throttle={}".format(val_t[ind_t_2]))
axs[5, 1].set(xlabel="Mach number", ylabel="SFC (x 1e-3 N/N/s)")
ind_h_list = [0, 4, 7, 10]
ind_h_list = [4, 7, 10]
ind_M_list = [0, 3, 6, 11]
ind_M_list = [3, 6, 11]
colors = ["b", "r", "g", "c", "m"]
# -----------------------------------------------------------------------------
# Throttle slices
for k, ind_h in enumerate(ind_h_list):
ind_M = ind_M_1
x = get_x(ind_M=ind_M, ind_h=ind_h)
y = interp.predict_values(x)
xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_h=ind_h)
axs[0, 0].plot(xt_, yt_, "o" + colors[k])
axs[0, 0].plot(lins_t, y[:, 0] / 1e6, colors[k])
xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_h=ind_h)
axs[0, 1].plot(xt_, yt_, "o" + colors[k])
axs[0, 1].plot(lins_t, y[:, 1] / 1e-4, colors[k])
ind_M = ind_M_2
x = get_x(ind_M=ind_M, ind_h=ind_h)
y = interp.predict_values(x)
xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_h=ind_h)
axs[1, 0].plot(xt_, yt_, "o" + colors[k])
axs[1, 0].plot(lins_t, y[:, 0] / 1e6, colors[k])
xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_h=ind_h)
axs[1, 1].plot(xt_, yt_, "o" + colors[k])
axs[1, 1].plot(lins_t, y[:, 1] / 1e-4, colors[k])
# -----------------------------------------------------------------------------
# Altitude slices
for k, ind_M in enumerate(ind_M_list):
ind_t = ind_t_1
x = get_x(ind_M=ind_M, ind_t=ind_t)
y = interp.predict_values(x)
xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_t=ind_t)
axs[2, 0].plot(xt_, yt_, "o" + colors[k])
axs[2, 0].plot(lins_h, y[:, 0] / 1e6, colors[k])
xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_t=ind_t)
axs[2, 1].plot(xt_, yt_, "o" + colors[k])
axs[2, 1].plot(lins_h, y[:, 1] / 1e-4, colors[k])
ind_t = ind_t_2
x = get_x(ind_M=ind_M, ind_t=ind_t)
y = interp.predict_values(x)
xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_t=ind_t)
axs[3, 0].plot(xt_, yt_, "o" + colors[k])
axs[3, 0].plot(lins_h, y[:, 0] / 1e6, colors[k])
xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_t=ind_t)
axs[3, 1].plot(xt_, yt_, "o" + colors[k])
axs[3, 1].plot(lins_h, y[:, 1] / 1e-4, colors[k])
# -----------------------------------------------------------------------------
# Mach number slices
for k, ind_h in enumerate(ind_h_list):
ind_t = ind_t_1
x = get_x(ind_t=ind_t, ind_h=ind_h)
y = interp.predict_values(x)
xt_, yt_ = get_pts(xt, yt, 0, ind_h=ind_h, ind_t=ind_t)
axs[4, 0].plot(xt_, yt_, "o" + colors[k])
axs[4, 0].plot(lins_M, y[:, 0] / 1e6, colors[k])
xt_, yt_ = get_pts(xt, yt, 1, ind_h=ind_h, ind_t=ind_t)
axs[4, 1].plot(xt_, yt_, "o" + colors[k])
axs[4, 1].plot(lins_M, y[:, 1] / 1e-4, colors[k])
ind_t = ind_t_2
x = get_x(ind_t=ind_t, ind_h=ind_h)
y = interp.predict_values(x)
xt_, yt_ = get_pts(xt, yt, 0, ind_h=ind_h, ind_t=ind_t)
axs[5, 0].plot(xt_, yt_, "o" + colors[k])
axs[5, 0].plot(lins_M, y[:, 0] / 1e6, colors[k])
xt_, yt_ = get_pts(xt, yt, 1, ind_h=ind_h, ind_t=ind_t)
axs[5, 1].plot(xt_, yt_, "o" + colors[k])
axs[5, 1].plot(lins_M, y[:, 1] / 1e-4, colors[k])
# -----------------------------------------------------------------------------
for k in range(2):
legend_entries = []
for ind_h in ind_h_list:
legend_entries.append("h={}".format(val_h[ind_h]))
legend_entries.append("")
axs[k, 0].legend(legend_entries)
axs[k, 1].legend(legend_entries)
axs[k + 4, 0].legend(legend_entries)
axs[k + 4, 1].legend(legend_entries)
legend_entries = []
for ind_M in ind_M_list:
legend_entries.append("M={}".format(val_M[ind_M]))
legend_entries.append("")
axs[k + 2, 0].legend(legend_entries)
axs[k + 2, 1].legend(legend_entries)
plt.show()
RMTB¶
from smt.surrogate_models import RMTB
from smt.examples.b777_engine.b777_engine import get_b777_engine, plot_b777_engine
xt, yt, dyt_dxt, xlimits = get_b777_engine()
interp = RMTB(
num_ctrl_pts=15,
xlimits=xlimits,
nonlinear_maxiter=20,
approx_order=2,
energy_weight=0e-14,
regularization_weight=0e-18,
extrapolate=True,
)
interp.set_training_values(xt, yt)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 0], 0)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 1], 1)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 2], 2)
interp.train()
plot_b777_engine(xt, yt, xlimits, interp)
___________________________________________________________________________
RMTB
___________________________________________________________________________
Problem size
# training points. : 1056
___________________________________________________________________________
Training
Training ...
Pre-computing matrices ...
Computing dof2coeff ...
Computing dof2coeff - done. Time (sec): 0.0000000
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0000000
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.1749866
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0050414
Pre-computing matrices - done. Time (sec): 0.1800280
Solving for degrees of freedom ...
Solving initial startup problem (n=3375) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 4.857178281e+07 2.642628384e+13
Iteration (num., iy, grad. norm, func.) : 0 0 1.371838165e+05 6.993448074e+09
Solving for output 0 - done. Time (sec): 0.0556653
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 3.711896708e-01 7.697335516e-04
Iteration (num., iy, grad. norm, func.) : 0 1 1.374254361e-03 3.512412267e-07
Solving for output 1 - done. Time (sec): 0.0594563
Solving initial startup problem (n=3375) - done. Time (sec): 0.1151216
Solving nonlinear problem (n=3375) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.371838165e+05 6.993448074e+09
Iteration (num., iy, grad. norm, func.) : 0 0 7.296273998e+04 1.952753642e+09
Iteration (num., iy, grad. norm, func.) : 1 0 4.699413853e+04 5.656526603e+08
Iteration (num., iy, grad. norm, func.) : 2 0 3.630530299e+04 3.867957232e+08
Iteration (num., iy, grad. norm, func.) : 3 0 3.132112066e+04 3.751114847e+08
Iteration (num., iy, grad. norm, func.) : 4 0 2.622497401e+04 3.233995367e+08
Iteration (num., iy, grad. norm, func.) : 5 0 1.632874586e+04 2.970251901e+08
Iteration (num., iy, grad. norm, func.) : 6 0 1.945828556e+04 2.644866975e+08
Iteration (num., iy, grad. norm, func.) : 7 0 7.031298294e+03 2.202296484e+08
Iteration (num., iy, grad. norm, func.) : 8 0 9.584176762e+03 2.010315328e+08
Iteration (num., iy, grad. norm, func.) : 9 0 6.066765838e+03 1.861519410e+08
Iteration (num., iy, grad. norm, func.) : 10 0 8.660248230e+03 1.774578981e+08
Iteration (num., iy, grad. norm, func.) : 11 0 5.959258886e+03 1.676188728e+08
Iteration (num., iy, grad. norm, func.) : 12 0 5.420215086e+03 1.611620261e+08
Iteration (num., iy, grad. norm, func.) : 13 0 3.047173881e+03 1.577535102e+08
Iteration (num., iy, grad. norm, func.) : 14 0 2.799164640e+03 1.565435585e+08
Iteration (num., iy, grad. norm, func.) : 15 0 3.613557675e+03 1.539615598e+08
Iteration (num., iy, grad. norm, func.) : 16 0 2.225395170e+03 1.512405059e+08
Iteration (num., iy, grad. norm, func.) : 17 0 2.043434052e+03 1.495341032e+08
Iteration (num., iy, grad. norm, func.) : 18 0 1.794946303e+03 1.489703531e+08
Iteration (num., iy, grad. norm, func.) : 19 0 1.362610245e+03 1.486885609e+08
Solving for output 0 - done. Time (sec): 1.1156650
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.374254361e-03 3.512412267e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.525988536e-04 6.188393994e-08
Iteration (num., iy, grad. norm, func.) : 1 1 3.057617946e-04 1.809764195e-08
Iteration (num., iy, grad. norm, func.) : 2 1 1.607604906e-04 8.481761160e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.400165707e-04 7.796139756e-09
Iteration (num., iy, grad. norm, func.) : 4 1 1.120967416e-04 6.716864259e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.164592243e-04 5.027554636e-09
Iteration (num., iy, grad. norm, func.) : 6 1 3.837990633e-05 2.869982182e-09
Iteration (num., iy, grad. norm, func.) : 7 1 5.592710013e-05 2.076645667e-09
Iteration (num., iy, grad. norm, func.) : 8 1 2.315187099e-05 1.822951599e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.598674619e-05 1.718454630e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.476820921e-05 1.578553884e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.173672750e-05 1.417305155e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.113086815e-05 1.297183916e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.971817939e-05 1.259939268e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.181193289e-05 1.237157107e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.205550947e-05 1.234213169e-09
Iteration (num., iy, grad. norm, func.) : 16 1 1.274996177e-05 1.207112873e-09
Iteration (num., iy, grad. norm, func.) : 17 1 1.006884757e-05 1.173920482e-09
Iteration (num., iy, grad. norm, func.) : 18 1 4.804334068e-06 1.146309226e-09
Iteration (num., iy, grad. norm, func.) : 19 1 4.607661308e-06 1.143931945e-09
Solving for output 1 - done. Time (sec): 1.1062367
Solving nonlinear problem (n=3375) - done. Time (sec): 2.2219017
Solving for degrees of freedom - done. Time (sec): 2.3370233
Training - done. Time (sec): 2.5170512
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0060544
Prediction time/pt. (sec) : 0.0000605
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0016241
Prediction time/pt. (sec) : 0.0000162
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0045381
Prediction time/pt. (sec) : 0.0000454
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000968
Prediction time/pt. (sec) : 0.0000010
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0068035
Prediction time/pt. (sec) : 0.0000680
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010366
Prediction time/pt. (sec) : 0.0000104
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0021219
Prediction time/pt. (sec) : 0.0000212
RMTC¶
from smt.surrogate_models import RMTC
from smt.examples.b777_engine.b777_engine import get_b777_engine, plot_b777_engine
xt, yt, dyt_dxt, xlimits = get_b777_engine()
interp = RMTC(
num_elements=6,
xlimits=xlimits,
nonlinear_maxiter=20,
approx_order=2,
energy_weight=0.0,
regularization_weight=0.0,
extrapolate=True,
)
interp.set_training_values(xt, yt)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 0], 0)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 1], 1)
interp.set_training_derivatives(xt, dyt_dxt[:, :, 2], 2)
interp.train()
plot_b777_engine(xt, yt, xlimits, interp)
___________________________________________________________________________
RMTC
___________________________________________________________________________
Problem size
# training points. : 1056
___________________________________________________________________________
Training
Training ...
Pre-computing matrices ...
Computing dof2coeff ...
Computing dof2coeff - done. Time (sec): 0.0151327
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0000000
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.1215806
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0404363
Pre-computing matrices - done. Time (sec): 0.1771495
Solving for degrees of freedom ...
Solving initial startup problem (n=2744) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 7.864862172e+07 2.642628384e+13
Iteration (num., iy, grad. norm, func.) : 0 0 1.954376733e+05 2.069307906e+09
Solving for output 0 - done. Time (sec): 0.1294446
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 8.095040141e-01 7.697335516e-04
Iteration (num., iy, grad. norm, func.) : 0 1 1.232503686e-03 1.322818515e-07
Solving for output 1 - done. Time (sec): 0.1287684
Solving initial startup problem (n=2744) - done. Time (sec): 0.2582130
Solving nonlinear problem (n=2744) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.954376733e+05 2.069307906e+09
Iteration (num., iy, grad. norm, func.) : 0 0 3.688989036e+04 4.210539534e+08
Iteration (num., iy, grad. norm, func.) : 1 0 1.711008392e+04 3.530483452e+08
Iteration (num., iy, grad. norm, func.) : 2 0 1.922847114e+04 3.504226099e+08
Iteration (num., iy, grad. norm, func.) : 3 0 9.537041405e+03 3.373561783e+08
Iteration (num., iy, grad. norm, func.) : 4 0 4.760722218e+03 3.327296410e+08
Iteration (num., iy, grad. norm, func.) : 5 0 6.376817973e+03 3.320587914e+08
Iteration (num., iy, grad. norm, func.) : 6 0 2.785458727e+03 3.312772871e+08
Iteration (num., iy, grad. norm, func.) : 7 0 2.159194888e+03 3.307195890e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.756134897e+03 3.304676354e+08
Iteration (num., iy, grad. norm, func.) : 9 0 2.544893553e+03 3.303514392e+08
Iteration (num., iy, grad. norm, func.) : 10 0 9.687040825e+02 3.302102085e+08
Iteration (num., iy, grad. norm, func.) : 11 0 1.529434541e+03 3.301310381e+08
Iteration (num., iy, grad. norm, func.) : 12 0 8.575442103e+02 3.300046689e+08
Iteration (num., iy, grad. norm, func.) : 13 0 7.946674618e+02 3.299019198e+08
Iteration (num., iy, grad. norm, func.) : 14 0 4.138160638e+02 3.298361385e+08
Iteration (num., iy, grad. norm, func.) : 15 0 3.344866005e+02 3.298229530e+08
Iteration (num., iy, grad. norm, func.) : 16 0 4.791739707e+02 3.298135435e+08
Iteration (num., iy, grad. norm, func.) : 17 0 7.683854023e+02 3.298069892e+08
Iteration (num., iy, grad. norm, func.) : 18 0 4.087822635e+02 3.298060925e+08
Iteration (num., iy, grad. norm, func.) : 19 0 2.867885276e+02 3.298024065e+08
Solving for output 0 - done. Time (sec): 2.5238924
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.232503686e-03 1.322818515e-07
Iteration (num., iy, grad. norm, func.) : 0 1 4.036053441e-04 9.595113849e-09
Iteration (num., iy, grad. norm, func.) : 1 1 2.706482615e-04 7.876088023e-09
Iteration (num., iy, grad. norm, func.) : 2 1 2.260119896e-04 6.102248425e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.174679565e-04 4.321455879e-09
Iteration (num., iy, grad. norm, func.) : 4 1 1.073558503e-04 4.060995795e-09
Iteration (num., iy, grad. norm, func.) : 5 1 6.275682902e-05 3.739333496e-09
Iteration (num., iy, grad. norm, func.) : 6 1 5.607076749e-05 3.359307617e-09
Iteration (num., iy, grad. norm, func.) : 7 1 3.901842383e-05 3.200980932e-09
Iteration (num., iy, grad. norm, func.) : 8 1 4.898053749e-05 3.121087196e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.789456822e-05 3.062451544e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.227200280e-05 3.044681794e-09
Iteration (num., iy, grad. norm, func.) : 11 1 3.096887425e-05 3.026565623e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.606775960e-05 2.993510820e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.554076288e-05 2.978354868e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.041250866e-05 2.953574603e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.155923771e-05 2.935360717e-09
Iteration (num., iy, grad. norm, func.) : 16 1 9.466047104e-06 2.927068596e-09
Iteration (num., iy, grad. norm, func.) : 17 1 9.185257136e-06 2.924961044e-09
Iteration (num., iy, grad. norm, func.) : 18 1 7.396047208e-06 2.924123759e-09
Iteration (num., iy, grad. norm, func.) : 19 1 1.605557201e-05 2.921915424e-09
Solving for output 1 - done. Time (sec): 2.5130031
Solving nonlinear problem (n=2744) - done. Time (sec): 5.0368955
Solving for degrees of freedom - done. Time (sec): 5.2951086
Training - done. Time (sec): 5.4749968
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009725
Prediction time/pt. (sec) : 0.0000097
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010002
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011082
Prediction time/pt. (sec) : 0.0000111
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0021155
Prediction time/pt. (sec) : 0.0000212
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0061014
Prediction time/pt. (sec) : 0.0000610
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0021162
Prediction time/pt. (sec) : 0.0000212
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0046735
Prediction time/pt. (sec) : 0.0000467
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0021138
Prediction time/pt. (sec) : 0.0000211
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0026135
Prediction time/pt. (sec) : 0.0000261
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0055439
Prediction time/pt. (sec) : 0.0000554
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
