Boeing 777 engine data set¶
import os
import numpy as np
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 matplotlib
import numpy as np
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(nrow, ncol, 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.examples.b777_engine.b777_engine import get_b777_engine, plot_b777_engine
from smt.surrogate_models import RMTB
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): 0.2145510
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0000000
Pre-computing matrices - done. Time (sec): 0.2145510
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 1.373632370e+05 6.994943224e+09
Solving for output 0 - done. Time (sec): 0.0749166
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 1.379731698e-03 3.522836449e-07
Solving for output 1 - done. Time (sec): 0.0908349
Solving initial startup problem (n=3375) - done. Time (sec): 0.1657515
Solving nonlinear problem (n=3375) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.373632370e+05 6.994943224e+09
Iteration (num., iy, grad. norm, func.) : 0 0 7.228762039e+04 1.953390951e+09
Iteration (num., iy, grad. norm, func.) : 1 0 4.731024753e+04 5.658049284e+08
Iteration (num., iy, grad. norm, func.) : 2 0 3.751054702e+04 3.886958030e+08
Iteration (num., iy, grad. norm, func.) : 3 0 3.359943786e+04 3.770962330e+08
Iteration (num., iy, grad. norm, func.) : 4 0 2.551184108e+04 3.265923277e+08
Iteration (num., iy, grad. norm, func.) : 5 0 1.763819113e+04 3.006483951e+08
Iteration (num., iy, grad. norm, func.) : 6 0 1.806072295e+04 2.662704404e+08
Iteration (num., iy, grad. norm, func.) : 7 0 8.625485033e+03 2.230481305e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.042753219e+04 2.025104184e+08
Iteration (num., iy, grad. norm, func.) : 9 0 8.520289391e+03 1.872411238e+08
Iteration (num., iy, grad. norm, func.) : 10 0 8.723807262e+03 1.766506426e+08
Iteration (num., iy, grad. norm, func.) : 11 0 7.552188302e+03 1.659406243e+08
Iteration (num., iy, grad. norm, func.) : 12 0 5.805156511e+03 1.622801583e+08
Iteration (num., iy, grad. norm, func.) : 13 0 3.760772225e+03 1.607839480e+08
Iteration (num., iy, grad. norm, func.) : 14 0 5.190020572e+03 1.603716663e+08
Iteration (num., iy, grad. norm, func.) : 15 0 3.526170085e+03 1.569770171e+08
Iteration (num., iy, grad. norm, func.) : 16 0 3.601650746e+03 1.535065970e+08
Iteration (num., iy, grad. norm, func.) : 17 0 1.450796571e+03 1.500561151e+08
Iteration (num., iy, grad. norm, func.) : 18 0 2.465781060e+03 1.490289858e+08
Iteration (num., iy, grad. norm, func.) : 19 0 1.706970844e+03 1.487533340e+08
Solving for output 0 - done. Time (sec): 1.5216210
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.379731698e-03 3.522836449e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.549321664e-04 6.189348135e-08
Iteration (num., iy, grad. norm, func.) : 1 1 3.033753847e-04 1.813290183e-08
Iteration (num., iy, grad. norm, func.) : 2 1 2.074294700e-04 8.416635233e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.679717689e-04 7.772256041e-09
Iteration (num., iy, grad. norm, func.) : 4 1 1.210384969e-04 6.709066740e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.013896403e-04 5.088687968e-09
Iteration (num., iy, grad. norm, func.) : 6 1 4.653467395e-05 2.986912993e-09
Iteration (num., iy, grad. norm, func.) : 7 1 5.102746264e-05 2.090009968e-09
Iteration (num., iy, grad. norm, func.) : 8 1 2.082183811e-05 1.785990436e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.819521568e-05 1.699600540e-09
Iteration (num., iy, grad. norm, func.) : 10 1 1.920704410e-05 1.598595555e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.286626885e-05 1.443383083e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.701209076e-05 1.308124029e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.559856977e-05 1.256780788e-09
Iteration (num., iy, grad. norm, func.) : 14 1 9.245693721e-06 1.231816967e-09
Iteration (num., iy, grad. norm, func.) : 15 1 9.360150345e-06 1.216232750e-09
Iteration (num., iy, grad. norm, func.) : 16 1 9.022769375e-06 1.188176708e-09
Iteration (num., iy, grad. norm, func.) : 17 1 7.918364377e-06 1.155510003e-09
Iteration (num., iy, grad. norm, func.) : 18 1 5.134927534e-06 1.140449521e-09
Iteration (num., iy, grad. norm, func.) : 19 1 6.357993355e-06 1.139634498e-09
Solving for output 1 - done. Time (sec): 1.5441484
Solving nonlinear problem (n=3375) - done. Time (sec): 3.0657694
Solving for degrees of freedom - done. Time (sec): 3.2315209
Training - done. Time (sec): 3.4460719
___________________________________________________________________________
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): 0.0080709
Prediction time/pt. (sec) : 0.0000807
___________________________________________________________________________
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): 0.0000000
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): 0.0051041
Prediction time/pt. (sec) : 0.0000510
___________________________________________________________________________
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): 0.0000000
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): 0.0080400
Prediction time/pt. (sec) : 0.0000804
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010188
Prediction time/pt. (sec) : 0.0000102
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
RMTC¶
from smt.examples.b777_engine.b777_engine import get_b777_engine, plot_b777_engine
from smt.surrogate_models import RMTC
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): 0.0221343
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0000000
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.1877089
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0567157
Pre-computing matrices - done. Time (sec): 0.2665589
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 2.029343879e+05 2.066646848e+09
Solving for output 0 - done. Time (sec): 0.1489806
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 1.333265997e-03 1.320416078e-07
Solving for output 1 - done. Time (sec): 0.2037950
Solving initial startup problem (n=2744) - done. Time (sec): 0.3527756
Solving nonlinear problem (n=2744) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 2.029343879e+05 2.066646848e+09
Iteration (num., iy, grad. norm, func.) : 0 0 3.382217141e+04 4.205527351e+08
Iteration (num., iy, grad. norm, func.) : 1 0 1.715751811e+04 3.531406723e+08
Iteration (num., iy, grad. norm, func.) : 2 0 1.961408652e+04 3.503658706e+08
Iteration (num., iy, grad. norm, func.) : 3 0 1.071978888e+04 3.373032349e+08
Iteration (num., iy, grad. norm, func.) : 4 0 4.813663988e+03 3.327146065e+08
Iteration (num., iy, grad. norm, func.) : 5 0 5.535339851e+03 3.320808460e+08
Iteration (num., iy, grad. norm, func.) : 6 0 4.310257739e+03 3.312895022e+08
Iteration (num., iy, grad. norm, func.) : 7 0 1.970509617e+03 3.307174337e+08
Iteration (num., iy, grad. norm, func.) : 8 0 2.087065844e+03 3.304717263e+08
Iteration (num., iy, grad. norm, func.) : 9 0 1.991997699e+03 3.303446843e+08
Iteration (num., iy, grad. norm, func.) : 10 0 9.702487316e+02 3.301998830e+08
Iteration (num., iy, grad. norm, func.) : 11 0 1.477775817e+03 3.301248228e+08
Iteration (num., iy, grad. norm, func.) : 12 0 8.216315742e+02 3.300010023e+08
Iteration (num., iy, grad. norm, func.) : 13 0 1.345080167e+03 3.298973205e+08
Iteration (num., iy, grad. norm, func.) : 14 0 4.575430141e+02 3.298315569e+08
Iteration (num., iy, grad. norm, func.) : 15 0 3.763188410e+02 3.298202017e+08
Iteration (num., iy, grad. norm, func.) : 16 0 7.044825455e+02 3.298133312e+08
Iteration (num., iy, grad. norm, func.) : 17 0 5.175024952e+02 3.298089323e+08
Iteration (num., iy, grad. norm, func.) : 18 0 5.659469259e+02 3.298071432e+08
Iteration (num., iy, grad. norm, func.) : 19 0 3.408424643e+02 3.298015516e+08
Solving for output 0 - done. Time (sec): 3.2044845
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.333265997e-03 1.320416078e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.969689807e-04 9.494233834e-09
Iteration (num., iy, grad. norm, func.) : 1 1 2.969731939e-04 7.887883745e-09
Iteration (num., iy, grad. norm, func.) : 2 1 2.611035743e-04 6.071186215e-09
Iteration (num., iy, grad. norm, func.) : 3 1 9.825052294e-05 4.309177186e-09
Iteration (num., iy, grad. norm, func.) : 4 1 9.008178910e-05 4.064996698e-09
Iteration (num., iy, grad. norm, func.) : 5 1 7.008779861e-05 3.745524434e-09
Iteration (num., iy, grad. norm, func.) : 6 1 4.156123324e-05 3.366608292e-09
Iteration (num., iy, grad. norm, func.) : 7 1 3.961930390e-05 3.208685455e-09
Iteration (num., iy, grad. norm, func.) : 8 1 4.319438376e-05 3.128213736e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.746662124e-05 3.067522541e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.215321541e-05 3.041279415e-09
Iteration (num., iy, grad. norm, func.) : 11 1 4.748356730e-05 3.032369678e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.687771835e-05 3.010213288e-09
Iteration (num., iy, grad. norm, func.) : 13 1 2.626855139e-05 2.989854076e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.125863347e-05 2.956369574e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.252003690e-05 2.937041725e-09
Iteration (num., iy, grad. norm, func.) : 16 1 9.968771466e-06 2.929476869e-09
Iteration (num., iy, grad. norm, func.) : 17 1 1.487456551e-05 2.926019676e-09
Iteration (num., iy, grad. norm, func.) : 18 1 5.180815992e-06 2.920834147e-09
Iteration (num., iy, grad. norm, func.) : 19 1 3.876107086e-06 2.920143941e-09
Solving for output 1 - done. Time (sec): 3.2836044
Solving nonlinear problem (n=2744) - done. Time (sec): 6.4880888
Solving for degrees of freedom - done. Time (sec): 6.8408644
Training - done. Time (sec): 7.1084726
___________________________________________________________________________
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): 0.0020499
Prediction time/pt. (sec) : 0.0000205
___________________________________________________________________________
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): 0.0060575
Prediction time/pt. (sec) : 0.0000606
___________________________________________________________________________
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): 0.0085499
Prediction time/pt. (sec) : 0.0000855
___________________________________________________________________________
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): 0.0020571
Prediction time/pt. (sec) : 0.0000206
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0050187
Prediction time/pt. (sec) : 0.0000502
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0030560
Prediction time/pt. (sec) : 0.0000306
___________________________________________________________________________
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): 0.0024855
Prediction time/pt. (sec) : 0.0000249
___________________________________________________________________________
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): 0.0065539
Prediction time/pt. (sec) : 0.0000655
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0020568
Prediction time/pt. (sec) : 0.0000206
