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): 0.1836782
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0108161
Pre-computing matrices - done. Time (sec): 0.1944942
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 1.286579719e+05 7.013975979e+09
Solving for output 0 - done. Time (sec): 0.0639007
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 1.604497664e-03 3.484939516e-07
Solving for output 1 - done. Time (sec): 0.0644658
Solving initial startup problem (n=3375) - done. Time (sec): 0.1283665
Solving nonlinear problem (n=3375) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.286579719e+05 7.013975979e+09
Iteration (num., iy, grad. norm, func.) : 0 0 7.469165704e+04 1.937634358e+09
Iteration (num., iy, grad. norm, func.) : 1 0 4.879509199e+04 5.667652516e+08
Iteration (num., iy, grad. norm, func.) : 2 0 3.396400747e+04 3.943728006e+08
Iteration (num., iy, grad. norm, func.) : 3 0 3.052422987e+04 3.847124210e+08
Iteration (num., iy, grad. norm, func.) : 4 0 2.707363528e+04 3.317086596e+08
Iteration (num., iy, grad. norm, func.) : 5 0 1.645948640e+04 3.031832603e+08
Iteration (num., iy, grad. norm, func.) : 6 0 1.639951693e+04 2.681480939e+08
Iteration (num., iy, grad. norm, func.) : 7 0 8.776758788e+03 2.237172457e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.634865326e+04 2.024493123e+08
Iteration (num., iy, grad. norm, func.) : 9 0 8.646698096e+03 1.871615537e+08
Iteration (num., iy, grad. norm, func.) : 10 0 8.194284160e+03 1.767782126e+08
Iteration (num., iy, grad. norm, func.) : 11 0 5.868982446e+03 1.653290075e+08
Iteration (num., iy, grad. norm, func.) : 12 0 4.588219094e+03 1.621900314e+08
Iteration (num., iy, grad. norm, func.) : 13 0 4.092068552e+03 1.612355794e+08
Iteration (num., iy, grad. norm, func.) : 14 0 4.471481177e+03 1.603871781e+08
Iteration (num., iy, grad. norm, func.) : 15 0 3.340521889e+03 1.563386070e+08
Iteration (num., iy, grad. norm, func.) : 16 0 2.934718911e+03 1.532056381e+08
Iteration (num., iy, grad. norm, func.) : 17 0 2.805515205e+03 1.503515957e+08
Iteration (num., iy, grad. norm, func.) : 18 0 3.652007244e+03 1.489115482e+08
Iteration (num., iy, grad. norm, func.) : 19 0 1.250904940e+03 1.487100656e+08
Solving for output 0 - done. Time (sec): 1.3380342
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.604497664e-03 3.484939516e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.447619583e-04 6.174104815e-08
Iteration (num., iy, grad. norm, func.) : 1 1 3.107502732e-04 1.811379903e-08
Iteration (num., iy, grad. norm, func.) : 2 1 2.525191337e-04 8.326216815e-09
Iteration (num., iy, grad. norm, func.) : 3 1 1.991058241e-04 7.664577421e-09
Iteration (num., iy, grad. norm, func.) : 4 1 1.283475478e-04 6.654611289e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.301435675e-04 5.046254626e-09
Iteration (num., iy, grad. norm, func.) : 6 1 5.300276103e-05 2.911821524e-09
Iteration (num., iy, grad. norm, func.) : 7 1 5.747185265e-05 2.069932273e-09
Iteration (num., iy, grad. norm, func.) : 8 1 2.034722845e-05 1.796844258e-09
Iteration (num., iy, grad. norm, func.) : 9 1 2.143725422e-05 1.708617806e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.843338883e-05 1.600036341e-09
Iteration (num., iy, grad. norm, func.) : 11 1 2.379422229e-05 1.430262554e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.553381972e-05 1.293472910e-09
Iteration (num., iy, grad. norm, func.) : 13 1 1.317173817e-05 1.262534422e-09
Iteration (num., iy, grad. norm, func.) : 14 1 1.175644327e-05 1.252466313e-09
Iteration (num., iy, grad. norm, func.) : 15 1 1.477319312e-05 1.233129278e-09
Iteration (num., iy, grad. norm, func.) : 16 1 6.779141172e-06 1.186980793e-09
Iteration (num., iy, grad. norm, func.) : 17 1 9.169780609e-06 1.163820652e-09
Iteration (num., iy, grad. norm, func.) : 18 1 5.457497938e-06 1.148218956e-09
Iteration (num., iy, grad. norm, func.) : 19 1 6.383583052e-06 1.141340222e-09
Solving for output 1 - done. Time (sec): 1.4260054
Solving nonlinear problem (n=3375) - done. Time (sec): 2.7640395
Solving for degrees of freedom - done. Time (sec): 2.8924060
Training - done. Time (sec): 3.0869002
___________________________________________________________________________
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): 0.0002041
Prediction time/pt. (sec) : 0.0000020
___________________________________________________________________________
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): 0.0009999
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009973
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009973
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0009966
Prediction time/pt. (sec) : 0.0000100
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0011568
Prediction time/pt. (sec) : 0.0000116
___________________________________________________________________________
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
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): 0.0216372
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0000000
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.1752834
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0524311
Pre-computing matrices - done. Time (sec): 0.2493517
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 2.016131802e+05 2.067144457e+09
Solving for output 0 - done. Time (sec): 0.1375966
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 1.369372950e-03 1.319294240e-07
Solving for output 1 - done. Time (sec): 0.1383369
Solving initial startup problem (n=2744) - done. Time (sec): 0.2759335
Solving nonlinear problem (n=2744) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 2.016131802e+05 2.067144457e+09
Iteration (num., iy, grad. norm, func.) : 0 0 3.050781756e+04 4.203956651e+08
Iteration (num., iy, grad. norm, func.) : 1 0 1.775862361e+04 3.531505777e+08
Iteration (num., iy, grad. norm, func.) : 2 0 1.907468303e+04 3.503624870e+08
Iteration (num., iy, grad. norm, func.) : 3 0 9.655279779e+03 3.373162250e+08
Iteration (num., iy, grad. norm, func.) : 4 0 5.062694953e+03 3.327169516e+08
Iteration (num., iy, grad. norm, func.) : 5 0 6.554180854e+03 3.320431226e+08
Iteration (num., iy, grad. norm, func.) : 6 0 2.564269504e+03 3.312605993e+08
Iteration (num., iy, grad. norm, func.) : 7 0 2.167873543e+03 3.307079148e+08
Iteration (num., iy, grad. norm, func.) : 8 0 1.646694990e+03 3.304613135e+08
Iteration (num., iy, grad. norm, func.) : 9 0 1.867577430e+03 3.303500055e+08
Iteration (num., iy, grad. norm, func.) : 10 0 1.210054705e+03 3.302115168e+08
Iteration (num., iy, grad. norm, func.) : 11 0 1.544261609e+03 3.301324967e+08
Iteration (num., iy, grad. norm, func.) : 12 0 7.705768299e+02 3.300091293e+08
Iteration (num., iy, grad. norm, func.) : 13 0 8.517446766e+02 3.298986759e+08
Iteration (num., iy, grad. norm, func.) : 14 0 4.218281099e+02 3.298287782e+08
Iteration (num., iy, grad. norm, func.) : 15 0 3.432443808e+02 3.298173834e+08
Iteration (num., iy, grad. norm, func.) : 16 0 4.866240749e+02 3.298109384e+08
Iteration (num., iy, grad. norm, func.) : 17 0 5.777224001e+02 3.298073509e+08
Iteration (num., iy, grad. norm, func.) : 18 0 5.610269680e+02 3.298050007e+08
Iteration (num., iy, grad. norm, func.) : 19 0 3.529550521e+02 3.297994272e+08
Solving for output 0 - done. Time (sec): 2.7543049
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.369372950e-03 1.319294240e-07
Iteration (num., iy, grad. norm, func.) : 0 1 3.441448381e-04 9.432333502e-09
Iteration (num., iy, grad. norm, func.) : 1 1 3.408829469e-04 7.831583554e-09
Iteration (num., iy, grad. norm, func.) : 2 1 2.881191379e-04 5.994974878e-09
Iteration (num., iy, grad. norm, func.) : 3 1 9.418174431e-05 4.285209848e-09
Iteration (num., iy, grad. norm, func.) : 4 1 9.696691230e-05 4.048149608e-09
Iteration (num., iy, grad. norm, func.) : 5 1 7.135640903e-05 3.725722643e-09
Iteration (num., iy, grad. norm, func.) : 6 1 4.563442180e-05 3.356718276e-09
Iteration (num., iy, grad. norm, func.) : 7 1 3.630648064e-05 3.203702923e-09
Iteration (num., iy, grad. norm, func.) : 8 1 3.786109878e-05 3.125756795e-09
Iteration (num., iy, grad. norm, func.) : 9 1 1.922251659e-05 3.063894004e-09
Iteration (num., iy, grad. norm, func.) : 10 1 2.548664540e-05 3.045559977e-09
Iteration (num., iy, grad. norm, func.) : 11 1 1.733387307e-05 3.034590139e-09
Iteration (num., iy, grad. norm, func.) : 12 1 1.766402961e-05 3.020924552e-09
Iteration (num., iy, grad. norm, func.) : 13 1 3.007220113e-05 2.989135543e-09
Iteration (num., iy, grad. norm, func.) : 14 1 8.798737329e-06 2.949646539e-09
Iteration (num., iy, grad. norm, func.) : 15 1 8.226312769e-06 2.933372502e-09
Iteration (num., iy, grad. norm, func.) : 16 1 1.001142480e-05 2.929711794e-09
Iteration (num., iy, grad. norm, func.) : 17 1 8.404076354e-06 2.927377118e-09
Iteration (num., iy, grad. norm, func.) : 18 1 7.835024335e-06 2.925044524e-09
Iteration (num., iy, grad. norm, func.) : 19 1 1.383915602e-05 2.922103115e-09
Solving for output 1 - done. Time (sec): 2.9016311
Solving nonlinear problem (n=2744) - done. Time (sec): 5.6559360
Solving for degrees of freedom - done. Time (sec): 5.9318695
Training - done. Time (sec): 6.1812212
___________________________________________________________________________
Evaluation
# eval points. : 100
Predicting ...
Predicting - done. Time (sec): 0.0024099
Prediction time/pt. (sec) : 0.0000241
___________________________________________________________________________
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): 0.0084071
Prediction time/pt. (sec) : 0.0000841
___________________________________________________________________________
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): 0.0023456
Prediction time/pt. (sec) : 0.0000235
___________________________________________________________________________
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): 0.0024068
Prediction time/pt. (sec) : 0.0000241
___________________________________________________________________________
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): 0.0024052
Prediction time/pt. (sec) : 0.0000241
___________________________________________________________________________
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): 0.0024068
Prediction time/pt. (sec) : 0.0000241