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): 0.2293053
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0075541
Pre-computing matrices - done. Time (sec): 0.2368593
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 1.373826973e+05 6.997915387e+09
Solving for output 0 - done. Time (sec): 0.0675280
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 1.395061018e-03 3.468699832e-07
Solving for output 1 - done. Time (sec): 0.0736167
Solving initial startup problem (n=3375) - done. Time (sec): 0.1416497
Solving nonlinear problem (n=3375) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.373826973e+05 6.997915387e+09
Iteration (num., iy, grad. norm, func.) : 0 0 7.235665692e+04 1.954806038e+09
Iteration (num., iy, grad. norm, func.) : 1 0 4.654098320e+04 5.658756761e+08
Iteration (num., iy, grad. norm, func.) : 2 0 3.672346133e+04 3.885491673e+08
Iteration (num., iy, grad. norm, func.) : 3 0 3.261616842e+04 3.768480084e+08
Iteration (num., iy, grad. norm, func.) : 4 0 2.686026706e+04 3.249773050e+08
Iteration (num., iy, grad. norm, func.) : 5 0 1.626148419e+04 2.983960747e+08
Iteration (num., iy, grad. norm, func.) : 6 0 1.524365745e+04 2.654419506e+08
Iteration (num., iy, grad. norm, func.) : 7 0 8.490561347e+03 2.216463966e+08
Iteration (num., iy, grad. norm, func.) : 8 0 9.545883104e+03 2.016764770e+08
Iteration (num., iy, grad. norm, func.) : 9 0 5.720345076e+03 1.864751429e+08
Iteration (num., iy, grad. norm, func.) : 10 0 8.662166329e+03 1.767845928e+08
Iteration (num., iy, grad. norm, func.) : 11 0 6.197316018e+03 1.659797529e+08
Iteration (num., iy, grad. norm, func.) : 12 0 4.224243819e+03 1.618341373e+08
Iteration (num., iy, grad. norm, func.) : 13 0 3.112670522e+03 1.600496853e+08
Iteration (num., iy, grad. norm, func.) : 14 0 4.370148466e+03 1.600262496e+08
Iteration (num., iy, grad. norm, func.) : 15 0 2.859520501e+03 1.569733173e+08
Iteration (num., iy, grad. norm, func.) : 16 0 2.782479646e+03 1.533014054e+08
Iteration (num., iy, grad. norm, func.) : 17 0 2.299670974e+03 1.496565883e+08
Iteration (num., iy, grad. norm, func.) : 18 0 1.610566561e+03 1.487769054e+08
Iteration (num., iy, grad. norm, func.) : 19 0 1.447300133e+03 1.485878967e+08
Solving for output 0 - done. Time (sec): 1.2917762
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.395061018e-03 3.468699832e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.914872455e-04 6.182312112e-08
Iteration (num., iy, grad. norm, func.) : 1 1 2.865874329e-04 1.805178232e-08
Iteration (num., iy, grad. norm, func.) : 2 1 2.247617079e-04 8.253323304e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.768547031e-04 7.596092771e-09
Iteration (num., iy, grad. norm, func.) : 4 1 1.254616429e-04 6.616975834e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.014353342e-04 5.078978077e-09
Iteration (num., iy, grad. norm, func.) : 6 1 4.561000928e-05 2.963531897e-09
Iteration (num., iy, grad. norm, func.) : 7 1 6.361066346e-05 2.080802088e-09
Iteration (num., iy, grad. norm, func.) : 8 1 2.006390508e-05 1.779385831e-09
Iteration (num., iy, grad. norm, func.) : 9 1 1.934234213e-05 1.701506695e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.059391901e-05 1.612436453e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.588418235e-05 1.449071076e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.072301170e-05 1.307094325e-09
Iteration (num., iy, grad. norm, func.) : 13 1 2.014181444e-05 1.265540786e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.119759769e-05 1.250124472e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.353578802e-05 1.231299250e-09
Iteration (num., iy, grad. norm, func.) : 16 1 1.274579638e-05 1.185742022e-09
Iteration (num., iy, grad. norm, func.) : 17 1 9.960075664e-06 1.162834806e-09
Iteration (num., iy, grad. norm, func.) : 18 1 5.406897757e-06 1.149088640e-09
Iteration (num., iy, grad. norm, func.) : 19 1 8.596626113e-06 1.145548481e-09
Solving for output 1 - done. Time (sec): 1.1725609
Solving nonlinear problem (n=3375) - done. Time (sec): 2.4643371
Solving for degrees of freedom - done. Time (sec): 2.6059868
Training - done. Time (sec): 2.8438473
___________________________________________________________________________
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): 0.0010023
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
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): 0.0010002
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009992
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009971
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
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): 0.0011530
Prediction time/pt. (sec) : 0.0000115
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010028
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010006
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
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): 0.0009995
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010352
Prediction time/pt. (sec) : 0.0000104
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010073
Prediction time/pt. (sec) : 0.0000101
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010004
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009961
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
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): 0.0221553
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0009987
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.1575251
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0562134
Pre-computing matrices - done. Time (sec): 0.2368925
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 2.013823241e+05 2.067229908e+09
Solving for output 0 - done. Time (sec): 0.1890025
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 1.423039559e-03 1.317122164e-07
Solving for output 1 - done. Time (sec): 0.2434375
Solving initial startup problem (n=2744) - done. Time (sec): 0.4324400
Solving nonlinear problem (n=2744) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 2.013823241e+05 2.067229908e+09
Iteration (num., iy, grad. norm, func.) : 0 0 3.142149731e+04 4.216257744e+08
Iteration (num., iy, grad. norm, func.) : 1 0 1.628819259e+04 3.528261743e+08
Iteration (num., iy, grad. norm, func.) : 2 0 2.431474045e+04 3.502779731e+08
Iteration (num., iy, grad. norm, func.) : 3 0 9.979393989e+03 3.372917623e+08
Iteration (num., iy, grad. norm, func.) : 4 0 4.504412583e+03 3.327215706e+08
Iteration (num., iy, grad. norm, func.) : 5 0 5.742398374e+03 3.320226809e+08
Iteration (num., iy, grad. norm, func.) : 6 0 2.634482225e+03 3.312305682e+08
Iteration (num., iy, grad. norm, func.) : 7 0 2.027481346e+03 3.307021257e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.164812990e+03 3.304671562e+08
Iteration (num., iy, grad. norm, func.) : 9 0 2.056698510e+03 3.303603150e+08
Iteration (num., iy, grad. norm, func.) : 10 0 1.588700733e+03 3.302176992e+08
Iteration (num., iy, grad. norm, func.) : 11 0 1.417683539e+03 3.301320906e+08
Iteration (num., iy, grad. norm, func.) : 12 0 8.464194882e+02 3.300058274e+08
Iteration (num., iy, grad. norm, func.) : 13 0 1.469318435e+03 3.299022133e+08
Iteration (num., iy, grad. norm, func.) : 14 0 3.776511399e+02 3.298368605e+08
Iteration (num., iy, grad. norm, func.) : 15 0 5.774489399e+02 3.298363026e+08
Iteration (num., iy, grad. norm, func.) : 16 0 4.650683101e+02 3.298262578e+08
Iteration (num., iy, grad. norm, func.) : 17 0 6.478920533e+02 3.298143199e+08
Iteration (num., iy, grad. norm, func.) : 18 0 3.655761629e+02 3.297990853e+08
Iteration (num., iy, grad. norm, func.) : 19 0 2.635230494e+02 3.297943728e+08
Solving for output 0 - done. Time (sec): 2.9683211
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.423039559e-03 1.317122164e-07
Iteration (num., iy, grad. norm, func.) : 0 1 4.801149326e-04 9.499902244e-09
Iteration (num., iy, grad. norm, func.) : 1 1 2.806984163e-04 7.828673651e-09
Iteration (num., iy, grad. norm, func.) : 2 1 2.607850877e-04 6.049600013e-09
Iteration (num., iy, grad. norm, func.) : 3 1 9.397660769e-05 4.307701931e-09
Iteration (num., iy, grad. norm, func.) : 4 1 8.401306727e-05 4.061384351e-09
Iteration (num., iy, grad. norm, func.) : 5 1 8.858356348e-05 3.737740437e-09
Iteration (num., iy, grad. norm, func.) : 6 1 4.418680845e-05 3.360984713e-09
Iteration (num., iy, grad. norm, func.) : 7 1 4.194077635e-05 3.204942422e-09
Iteration (num., iy, grad. norm, func.) : 8 1 5.270100036e-05 3.122969531e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.806077101e-05 3.065922721e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.302063128e-05 3.043676807e-09
Iteration (num., iy, grad. norm, func.) : 11 1 3.443713096e-05 3.035276555e-09
Iteration (num., iy, grad. norm, func.) : 12 1 2.187733093e-05 3.018521234e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.908237935e-05 2.990285088e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.556075444e-05 2.957239892e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.298701588e-05 2.936695843e-09
Iteration (num., iy, grad. norm, func.) : 16 1 8.194155021e-06 2.928570971e-09
Iteration (num., iy, grad. norm, func.) : 17 1 9.208864453e-06 2.925633327e-09
Iteration (num., iy, grad. norm, func.) : 18 1 7.771173950e-06 2.923701376e-09
Iteration (num., iy, grad. norm, func.) : 19 1 1.062166843e-05 2.920674153e-09
Solving for output 1 - done. Time (sec): 2.7008305
Solving nonlinear problem (n=2744) - done. Time (sec): 5.6691515
Solving for degrees of freedom - done. Time (sec): 6.1015916
Training - done. Time (sec): 6.3395131
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010016
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0019357
Prediction time/pt. (sec) : 0.0000194
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0019703
Prediction time/pt. (sec) : 0.0000197
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0019977
Prediction time/pt. (sec) : 0.0000200
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010009
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010037
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0019994
Prediction time/pt. (sec) : 0.0000200
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010023
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010011
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0020323
Prediction time/pt. (sec) : 0.0000203
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010257
Prediction time/pt. (sec) : 0.0000103
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0020003
Prediction time/pt. (sec) : 0.0000200
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009995
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010004
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0019758
Prediction time/pt. (sec) : 0.0000198
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0010316
Prediction time/pt. (sec) : 0.0000103
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009997
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0019929
Prediction time/pt. (sec) : 0.0000199
