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): 0.0000029
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0005333
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.2833576
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0102527
Pre-computing matrices - done. Time (sec): 0.2942176
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 1.364349733e+05 7.002441710e+09
Solving for output 0 - done. Time (sec): 0.0885880
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 1.384257034e-03 3.512467641e-07
Solving for output 1 - done. Time (sec): 0.0884008
Solving initial startup problem (n=3375) - done. Time (sec): 0.1770794
Solving nonlinear problem (n=3375) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.364349733e+05 7.002441710e+09
Iteration (num., iy, grad. norm, func.) : 0 0 7.401682427e+04 1.956585489e+09
Iteration (num., iy, grad. norm, func.) : 1 0 4.640761309e+04 5.653768085e+08
Iteration (num., iy, grad. norm, func.) : 2 0 3.726949662e+04 3.860194807e+08
Iteration (num., iy, grad. norm, func.) : 3 0 3.244331543e+04 3.735217325e+08
Iteration (num., iy, grad. norm, func.) : 4 0 2.356309977e+04 3.232040667e+08
Iteration (num., iy, grad. norm, func.) : 5 0 1.896770441e+04 2.970854602e+08
Iteration (num., iy, grad. norm, func.) : 6 0 1.168979712e+04 2.643923864e+08
Iteration (num., iy, grad. norm, func.) : 7 0 1.199133401e+04 2.223771115e+08
Iteration (num., iy, grad. norm, func.) : 8 0 9.363877631e+03 2.013234589e+08
Iteration (num., iy, grad. norm, func.) : 9 0 9.544160641e+03 1.861724031e+08
Iteration (num., iy, grad. norm, func.) : 10 0 9.458916793e+03 1.762819815e+08
Iteration (num., iy, grad. norm, func.) : 11 0 4.152198214e+03 1.661887141e+08
Iteration (num., iy, grad. norm, func.) : 12 0 8.359804107e+03 1.619868009e+08
Iteration (num., iy, grad. norm, func.) : 13 0 2.678073894e+03 1.599839425e+08
Iteration (num., iy, grad. norm, func.) : 14 0 2.301049932e+03 1.583627245e+08
Iteration (num., iy, grad. norm, func.) : 15 0 3.127472449e+03 1.554361115e+08
Iteration (num., iy, grad. norm, func.) : 16 0 2.879195835e+03 1.516054749e+08
Iteration (num., iy, grad. norm, func.) : 17 0 1.583184160e+03 1.493412967e+08
Iteration (num., iy, grad. norm, func.) : 18 0 2.202973513e+03 1.492035778e+08
Iteration (num., iy, grad. norm, func.) : 19 0 1.397841194e+03 1.489828558e+08
Solving for output 0 - done. Time (sec): 1.7724838
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.384257034e-03 3.512467641e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.575138262e-04 6.166597300e-08
Iteration (num., iy, grad. norm, func.) : 1 1 3.156992731e-04 1.817140551e-08
Iteration (num., iy, grad. norm, func.) : 2 1 2.070220585e-04 8.504635606e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.711558893e-04 7.824284644e-09
Iteration (num., iy, grad. norm, func.) : 4 1 1.147466159e-04 6.729973912e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.033293877e-04 5.063463186e-09
Iteration (num., iy, grad. norm, func.) : 6 1 5.272698157e-05 2.929839938e-09
Iteration (num., iy, grad. norm, func.) : 7 1 4.894442104e-05 2.071717930e-09
Iteration (num., iy, grad. norm, func.) : 8 1 2.850823295e-05 1.797321609e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.566163204e-05 1.713105879e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.728118053e-05 1.606498899e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.407731298e-05 1.439553327e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.588414550e-05 1.302254672e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.941516089e-05 1.258276496e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.159190980e-05 1.239434907e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.872674427e-05 1.235569556e-09
Iteration (num., iy, grad. norm, func.) : 16 1 1.169536710e-05 1.206341167e-09
Iteration (num., iy, grad. norm, func.) : 17 1 1.005666171e-05 1.172498758e-09
Iteration (num., iy, grad. norm, func.) : 18 1 4.240888944e-06 1.143928197e-09
Iteration (num., iy, grad. norm, func.) : 19 1 4.653082813e-06 1.142989811e-09
Solving for output 1 - done. Time (sec): 1.7775493
Solving nonlinear problem (n=3375) - done. Time (sec): 3.5501003
Solving for degrees of freedom - done. Time (sec): 3.7272718
Training - done. Time (sec): 4.0223479
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012805
Prediction time/pt. (sec) : 0.0000128
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011656
Prediction time/pt. (sec) : 0.0000117
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011556
Prediction time/pt. (sec) : 0.0000116
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011547
Prediction time/pt. (sec) : 0.0000115
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011721
Prediction time/pt. (sec) : 0.0000117
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011621
Prediction time/pt. (sec) : 0.0000116
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011656
Prediction time/pt. (sec) : 0.0000117
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011466
Prediction time/pt. (sec) : 0.0000115
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012228
Prediction time/pt. (sec) : 0.0000122
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012078
Prediction time/pt. (sec) : 0.0000121
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012228
Prediction time/pt. (sec) : 0.0000122
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012078
Prediction time/pt. (sec) : 0.0000121
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012197
Prediction time/pt. (sec) : 0.0000122
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012212
Prediction time/pt. (sec) : 0.0000122
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012045
Prediction time/pt. (sec) : 0.0000120
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012193
Prediction time/pt. (sec) : 0.0000122
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012119
Prediction time/pt. (sec) : 0.0000121
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0012147
Prediction time/pt. (sec) : 0.0000121
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): 0.0221884
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0005217
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.1968417
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0910423
Pre-computing matrices - done. Time (sec): 0.3106997
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 2.020804204e+05 2.067017787e+09
Solving for output 0 - done. Time (sec): 0.1912801
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 1.242052177e-03 1.322622537e-07
Solving for output 1 - done. Time (sec): 0.1920962
Solving initial startup problem (n=2744) - done. Time (sec): 0.3834834
Solving nonlinear problem (n=2744) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 2.020804204e+05 2.067017787e+09
Iteration (num., iy, grad. norm, func.) : 0 0 3.795378678e+04 4.209003076e+08
Iteration (num., iy, grad. norm, func.) : 1 0 1.691388107e+04 3.530609622e+08
Iteration (num., iy, grad. norm, func.) : 2 0 2.424674299e+04 3.502442863e+08
Iteration (num., iy, grad. norm, func.) : 3 0 1.058301522e+04 3.371492582e+08
Iteration (num., iy, grad. norm, func.) : 4 0 4.312724954e+03 3.326822270e+08
Iteration (num., iy, grad. norm, func.) : 5 0 5.977501584e+03 3.320622264e+08
Iteration (num., iy, grad. norm, func.) : 6 0 2.564093348e+03 3.312761931e+08
Iteration (num., iy, grad. norm, func.) : 7 0 2.633911637e+03 3.307130145e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.496166349e+03 3.304586905e+08
Iteration (num., iy, grad. norm, func.) : 9 0 1.842768951e+03 3.303544335e+08
Iteration (num., iy, grad. norm, func.) : 10 0 1.051264756e+03 3.302209836e+08
Iteration (num., iy, grad. norm, func.) : 11 0 1.324634199e+03 3.301346076e+08
Iteration (num., iy, grad. norm, func.) : 12 0 9.434603444e+02 3.299980929e+08
Iteration (num., iy, grad. norm, func.) : 13 0 8.313796402e+02 3.299030047e+08
Iteration (num., iy, grad. norm, func.) : 14 0 5.486546277e+02 3.298453756e+08
Iteration (num., iy, grad. norm, func.) : 15 0 6.551215009e+02 3.298396348e+08
Iteration (num., iy, grad. norm, func.) : 16 0 4.500885038e+02 3.298378066e+08
Iteration (num., iy, grad. norm, func.) : 17 0 1.028007688e+03 3.298282370e+08
Iteration (num., iy, grad. norm, func.) : 18 0 3.399838938e+02 3.298087400e+08
Iteration (num., iy, grad. norm, func.) : 19 0 2.053750868e+02 3.298071682e+08
Solving for output 0 - done. Time (sec): 3.7802360
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.242052177e-03 1.322622537e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.336667748e-04 9.461988364e-09
Iteration (num., iy, grad. norm, func.) : 1 1 3.413078795e-04 7.888237722e-09
Iteration (num., iy, grad. norm, func.) : 2 1 2.136195000e-04 6.077625043e-09
Iteration (num., iy, grad. norm, func.) : 3 1 8.542812525e-05 4.320340731e-09
Iteration (num., iy, grad. norm, func.) : 4 1 8.048002545e-05 4.069045408e-09
Iteration (num., iy, grad. norm, func.) : 5 1 5.885314829e-05 3.747906307e-09
Iteration (num., iy, grad. norm, func.) : 6 1 4.591463641e-05 3.368137289e-09
Iteration (num., iy, grad. norm, func.) : 7 1 3.900587215e-05 3.208417254e-09
Iteration (num., iy, grad. norm, func.) : 8 1 4.559233031e-05 3.125987804e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.617598725e-05 3.067238678e-09
Iteration (num., iy, grad. norm, func.) : 10 1 3.747446115e-05 3.046046092e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.361552341e-05 3.036950106e-09
Iteration (num., iy, grad. norm, func.) : 12 1 2.753651777e-05 3.019194609e-09
Iteration (num., iy, grad. norm, func.) : 13 1 2.186673375e-05 2.992432238e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.772397035e-05 2.960899015e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.070987545e-05 2.936477461e-09
Iteration (num., iy, grad. norm, func.) : 16 1 9.065950919e-06 2.926874777e-09
Iteration (num., iy, grad. norm, func.) : 17 1 7.400482695e-06 2.924517350e-09
Iteration (num., iy, grad. norm, func.) : 18 1 6.317494830e-06 2.922681890e-09
Iteration (num., iy, grad. norm, func.) : 19 1 1.047966941e-05 2.918951548e-09
Solving for output 1 - done. Time (sec): 3.7826016
Solving nonlinear problem (n=2744) - done. Time (sec): 7.5629272
Solving for degrees of freedom - done. Time (sec): 7.9465067
Training - done. Time (sec): 8.2599399
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0029576
Prediction time/pt. (sec) : 0.0000296
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0028090
Prediction time/pt. (sec) : 0.0000281
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0028059
Prediction time/pt. (sec) : 0.0000281
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0027966
Prediction time/pt. (sec) : 0.0000280
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0029519
Prediction time/pt. (sec) : 0.0000295
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0029473
Prediction time/pt. (sec) : 0.0000295
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0027869
Prediction time/pt. (sec) : 0.0000279
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0025973
Prediction time/pt. (sec) : 0.0000260
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0028708
Prediction time/pt. (sec) : 0.0000287
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0027230
Prediction time/pt. (sec) : 0.0000272
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0027533
Prediction time/pt. (sec) : 0.0000275
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0026200
Prediction time/pt. (sec) : 0.0000262
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0028691
Prediction time/pt. (sec) : 0.0000287
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0027015
Prediction time/pt. (sec) : 0.0000270
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0028729
Prediction time/pt. (sec) : 0.0000287
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0027158
Prediction time/pt. (sec) : 0.0000272
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0030129
Prediction time/pt. (sec) : 0.0000301
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0028460
Prediction time/pt. (sec) : 0.0000285
