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(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.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): 0.0001721
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.0877221
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0030839
Pre-computing matrices - done. Time (sec): 0.0910089
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 1.293420813e+05 7.013297605e+09
Solving for output 0 - done. Time (sec): 0.0403881
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 1.591831008e-03 3.481810730e-07
Solving for output 1 - done. Time (sec): 0.0402641
Solving initial startup problem (n=3375) - done. Time (sec): 0.0806851
Solving nonlinear problem (n=3375) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.293420813e+05 7.013297605e+09
Iteration (num., iy, grad. norm, func.) : 0 0 9.918153307e+04 1.946328603e+09
Iteration (num., iy, grad. norm, func.) : 1 0 4.760511919e+04 5.642222750e+08
Iteration (num., iy, grad. norm, func.) : 2 0 3.509905718e+04 3.916910153e+08
Iteration (num., iy, grad. norm, func.) : 3 0 3.266254986e+04 3.812681287e+08
Iteration (num., iy, grad. norm, func.) : 4 0 2.433474999e+04 3.307625875e+08
Iteration (num., iy, grad. norm, func.) : 5 0 1.836831901e+04 3.044379206e+08
Iteration (num., iy, grad. norm, func.) : 6 0 1.369130884e+04 2.694361513e+08
Iteration (num., iy, grad. norm, func.) : 7 0 1.189289106e+04 2.253292276e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.348951232e+04 2.030170277e+08
Iteration (num., iy, grad. norm, func.) : 9 0 9.749280003e+03 1.871694192e+08
Iteration (num., iy, grad. norm, func.) : 10 0 7.933559876e+03 1.773445232e+08
Iteration (num., iy, grad. norm, func.) : 11 0 6.264482674e+03 1.676226215e+08
Iteration (num., iy, grad. norm, func.) : 12 0 8.774953813e+03 1.628378170e+08
Iteration (num., iy, grad. norm, func.) : 13 0 3.157944089e+03 1.602058800e+08
Iteration (num., iy, grad. norm, func.) : 14 0 2.774402118e+03 1.586039661e+08
Iteration (num., iy, grad. norm, func.) : 15 0 4.092421745e+03 1.557881701e+08
Iteration (num., iy, grad. norm, func.) : 16 0 4.055495308e+03 1.520351077e+08
Iteration (num., iy, grad. norm, func.) : 17 0 1.671677051e+03 1.497665190e+08
Iteration (num., iy, grad. norm, func.) : 18 0 2.100834104e+03 1.492367468e+08
Iteration (num., iy, grad. norm, func.) : 19 0 1.848493546e+03 1.492015420e+08
Solving for output 0 - done. Time (sec): 0.8023741
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.591831008e-03 3.481810730e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.662651803e-04 6.180892232e-08
Iteration (num., iy, grad. norm, func.) : 1 1 3.003014390e-04 1.793235061e-08
Iteration (num., iy, grad. norm, func.) : 2 1 1.990534749e-04 8.250975285e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.623714955e-04 7.620571122e-09
Iteration (num., iy, grad. norm, func.) : 4 1 1.143352248e-04 6.621461165e-09
Iteration (num., iy, grad. norm, func.) : 5 1 9.551202845e-05 5.069094900e-09
Iteration (num., iy, grad. norm, func.) : 6 1 4.609982291e-05 2.979488927e-09
Iteration (num., iy, grad. norm, func.) : 7 1 4.024524802e-05 2.089470591e-09
Iteration (num., iy, grad. norm, func.) : 8 1 2.160993922e-05 1.783815797e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.575210915e-05 1.704417034e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.139027152e-05 1.618671689e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.613440336e-05 1.455574136e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.182699498e-05 1.311973808e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.342778429e-05 1.264965460e-09
Iteration (num., iy, grad. norm, func.) : 14 1 9.972369963e-06 1.245134658e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.799078033e-05 1.237777758e-09
Iteration (num., iy, grad. norm, func.) : 16 1 9.400245015e-06 1.203431806e-09
Iteration (num., iy, grad. norm, func.) : 17 1 1.153928975e-05 1.173616633e-09
Iteration (num., iy, grad. norm, func.) : 18 1 4.709245007e-06 1.148425945e-09
Iteration (num., iy, grad. norm, func.) : 19 1 7.972659809e-06 1.144617612e-09
Solving for output 1 - done. Time (sec): 0.8020101
Solving nonlinear problem (n=3375) - done. Time (sec): 1.6045878
Solving for degrees of freedom - done. Time (sec): 1.6853032
Training - done. Time (sec): 1.7767541
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0004091
Prediction time/pt. (sec) : 0.0000041
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003400
Prediction time/pt. (sec) : 0.0000034
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003510
Prediction time/pt. (sec) : 0.0000035
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003362
Prediction time/pt. (sec) : 0.0000034
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003319
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003319
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003321
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003319
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003369
Prediction time/pt. (sec) : 0.0000034
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003309
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003331
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003290
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003328
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003321
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003319
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003309
Prediction time/pt. (sec) : 0.0000033
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003362
Prediction time/pt. (sec) : 0.0000034
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0003428
Prediction time/pt. (sec) : 0.0000034
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): 0.0094349
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0001671
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.0779128
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0441880
Pre-computing matrices - done. Time (sec): 0.1317570
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 2.052317109e+05 2.069396782e+09
Solving for output 0 - done. Time (sec): 0.1094899
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 1.273662871e-03 1.329452358e-07
Solving for output 1 - done. Time (sec): 0.0945330
Solving initial startup problem (n=2744) - done. Time (sec): 0.2048051
Solving nonlinear problem (n=2744) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 2.052317109e+05 2.069396782e+09
Iteration (num., iy, grad. norm, func.) : 0 0 2.996366083e+04 4.219824562e+08
Iteration (num., iy, grad. norm, func.) : 1 0 2.262504236e+04 3.529144002e+08
Iteration (num., iy, grad. norm, func.) : 2 0 2.631491561e+04 3.502952705e+08
Iteration (num., iy, grad. norm, func.) : 3 0 1.025035947e+04 3.373979635e+08
Iteration (num., iy, grad. norm, func.) : 4 0 5.035075808e+03 3.327895836e+08
Iteration (num., iy, grad. norm, func.) : 5 0 5.472210008e+03 3.320446921e+08
Iteration (num., iy, grad. norm, func.) : 6 0 2.744180795e+03 3.312471530e+08
Iteration (num., iy, grad. norm, func.) : 7 0 2.263098458e+03 3.307156644e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.386933927e+03 3.304724667e+08
Iteration (num., iy, grad. norm, func.) : 9 0 2.385520118e+03 3.303609813e+08
Iteration (num., iy, grad. norm, func.) : 10 0 1.218967550e+03 3.302206905e+08
Iteration (num., iy, grad. norm, func.) : 11 0 1.564832719e+03 3.301333245e+08
Iteration (num., iy, grad. norm, func.) : 12 0 7.375264475e+02 3.300031216e+08
Iteration (num., iy, grad. norm, func.) : 13 0 7.105096838e+02 3.299092956e+08
Iteration (num., iy, grad. norm, func.) : 14 0 5.063300804e+02 3.298500949e+08
Iteration (num., iy, grad. norm, func.) : 15 0 6.753465494e+02 3.298430233e+08
Iteration (num., iy, grad. norm, func.) : 16 0 5.894933008e+02 3.298417638e+08
Iteration (num., iy, grad. norm, func.) : 17 0 8.077216254e+02 3.298310170e+08
Iteration (num., iy, grad. norm, func.) : 18 0 3.516219330e+02 3.298104228e+08
Iteration (num., iy, grad. norm, func.) : 19 0 2.812559106e+02 3.298070074e+08
Solving for output 0 - done. Time (sec): 1.7558539
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.273662871e-03 1.329452358e-07
Iteration (num., iy, grad. norm, func.) : 0 1 4.370977543e-04 9.590212540e-09
Iteration (num., iy, grad. norm, func.) : 1 1 3.535273152e-04 8.038446158e-09
Iteration (num., iy, grad. norm, func.) : 2 1 2.820354261e-04 6.088243046e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.018171021e-04 4.300815041e-09
Iteration (num., iy, grad. norm, func.) : 4 1 8.071151343e-05 4.077142295e-09
Iteration (num., iy, grad. norm, func.) : 5 1 7.162243229e-05 3.761864806e-09
Iteration (num., iy, grad. norm, func.) : 6 1 5.255169110e-05 3.370801596e-09
Iteration (num., iy, grad. norm, func.) : 7 1 4.368472782e-05 3.207626194e-09
Iteration (num., iy, grad. norm, func.) : 8 1 3.611768378e-05 3.127888941e-09
Iteration (num., iy, grad. norm, func.) : 9 1 4.734334534e-05 3.070805709e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.171968345e-05 3.040709354e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.696172674e-05 3.033491503e-09
Iteration (num., iy, grad. norm, func.) : 12 1 2.348198700e-05 3.011700927e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.947889207e-05 2.993904593e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.673014568e-05 2.964466175e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.446505038e-05 2.938848122e-09
Iteration (num., iy, grad. norm, func.) : 16 1 8.414603397e-06 2.925003780e-09
Iteration (num., iy, grad. norm, func.) : 17 1 6.862887355e-06 2.923601781e-09
Iteration (num., iy, grad. norm, func.) : 18 1 7.023657912e-06 2.922484797e-09
Iteration (num., iy, grad. norm, func.) : 19 1 9.554151439e-06 2.920588525e-09
Solving for output 1 - done. Time (sec): 1.7513719
Solving nonlinear problem (n=2744) - done. Time (sec): 3.5072529
Solving for degrees of freedom - done. Time (sec): 3.7120879
Training - done. Time (sec): 3.8448641
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0008390
Prediction time/pt. (sec) : 0.0000084
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0008519
Prediction time/pt. (sec) : 0.0000085
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007892
Prediction time/pt. (sec) : 0.0000079
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007849
Prediction time/pt. (sec) : 0.0000078
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007589
Prediction time/pt. (sec) : 0.0000076
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007491
Prediction time/pt. (sec) : 0.0000075
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0006919
Prediction time/pt. (sec) : 0.0000069
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007019
Prediction time/pt. (sec) : 0.0000070
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007629
Prediction time/pt. (sec) : 0.0000076
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0008049
Prediction time/pt. (sec) : 0.0000080
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0006881
Prediction time/pt. (sec) : 0.0000069
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007041
Prediction time/pt. (sec) : 0.0000070
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007851
Prediction time/pt. (sec) : 0.0000079
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007961
Prediction time/pt. (sec) : 0.0000080
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007818
Prediction time/pt. (sec) : 0.0000078
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007949
Prediction time/pt. (sec) : 0.0000079
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007670
Prediction time/pt. (sec) : 0.0000077
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0007880
Prediction time/pt. (sec) : 0.0000079
