RANS CRM wing 2-D data set¶
import numpy as np
raw = np.array(
[
[
2.000000000000000000e00,
4.500000000000000111e-01,
1.536799999999999972e-02,
3.674239999999999728e-01,
5.592279999999999474e-01,
-1.258039999999999992e-01,
-1.248699999999999984e-02,
],
[
3.500000000000000000e00,
4.500000000000000111e-01,
1.985100000000000059e-02,
4.904470000000000218e-01,
7.574600000000000222e-01,
-1.615260000000000029e-01,
8.987000000000000197e-03,
],
[
5.000000000000000000e00,
4.500000000000000111e-01,
2.571000000000000021e-02,
6.109189999999999898e-01,
9.497949999999999449e-01,
-1.954619999999999969e-01,
4.090900000000000092e-02,
],
[
6.500000000000000000e00,
4.500000000000000111e-01,
3.304200000000000192e-02,
7.266120000000000356e-01,
1.131138999999999895e00,
-2.255890000000000117e-01,
8.185399999999999621e-02,
],
[
8.000000000000000000e00,
4.500000000000000111e-01,
4.318999999999999923e-02,
8.247250000000000414e-01,
1.271487000000000034e00,
-2.397040000000000004e-01,
1.217659999999999992e-01,
],
[
0.000000000000000000e00,
5.799999999999999600e-01,
1.136200000000000057e-02,
2.048760000000000026e-01,
2.950280000000000125e-01,
-7.882100000000000217e-02,
-2.280099999999999835e-02,
],
[
1.500000000000000000e00,
5.799999999999999600e-01,
1.426000000000000011e-02,
3.375619999999999732e-01,
5.114130000000000065e-01,
-1.189420000000000061e-01,
-1.588200000000000028e-02,
],
[
3.000000000000000000e00,
5.799999999999999600e-01,
1.866400000000000003e-02,
4.687450000000000228e-01,
7.240400000000000169e-01,
-1.577669999999999906e-01,
3.099999999999999891e-03,
],
[
4.500000000000000000e00,
5.799999999999999600e-01,
2.461999999999999952e-02,
5.976639999999999731e-01,
9.311709999999999710e-01,
-1.944160000000000055e-01,
3.357500000000000068e-02,
],
[
6.000000000000000000e00,
5.799999999999999600e-01,
3.280700000000000283e-02,
7.142249999999999988e-01,
1.111707999999999918e00,
-2.205870000000000053e-01,
7.151699999999999724e-02,
],
[
0.000000000000000000e00,
6.800000000000000488e-01,
1.138800000000000055e-02,
2.099310000000000065e-01,
3.032230000000000203e-01,
-8.187899999999999345e-02,
-2.172699999999999979e-02,
],
[
1.500000000000000000e00,
6.800000000000000488e-01,
1.458699999999999927e-02,
3.518569999999999753e-01,
5.356630000000000003e-01,
-1.257649999999999879e-01,
-1.444800000000000077e-02,
],
[
3.000000000000000000e00,
6.800000000000000488e-01,
1.952800000000000022e-02,
4.924879999999999813e-01,
7.644769999999999621e-01,
-1.678040000000000087e-01,
6.023999999999999841e-03,
],
[
4.500000000000000000e00,
6.800000000000000488e-01,
2.666699999999999973e-02,
6.270339999999999803e-01,
9.801630000000000065e-01,
-2.035240000000000105e-01,
3.810000000000000192e-02,
],
[
6.000000000000000000e00,
6.800000000000000488e-01,
3.891800000000000120e-02,
7.172730000000000494e-01,
1.097855999999999943e00,
-2.014620000000000022e-01,
6.640000000000000069e-02,
],
[
0.000000000000000000e00,
7.500000000000000000e-01,
1.150699999999999987e-02,
2.149069999999999869e-01,
3.115740000000000176e-01,
-8.498999999999999611e-02,
-2.057700000000000154e-02,
],
[
1.250000000000000000e00,
7.500000000000000000e-01,
1.432600000000000019e-02,
3.415969999999999840e-01,
5.199390000000000400e-01,
-1.251009999999999900e-01,
-1.515400000000000080e-02,
],
[
2.500000000000000000e00,
7.500000000000000000e-01,
1.856000000000000011e-02,
4.677589999999999804e-01,
7.262499999999999512e-01,
-1.635169999999999957e-01,
3.989999999999999949e-04,
],
[
3.750000000000000000e00,
7.500000000000000000e-01,
2.472399999999999945e-02,
5.911459999999999493e-01,
9.254930000000000101e-01,
-1.966150000000000120e-01,
2.524900000000000061e-02,
],
[
5.000000000000000000e00,
7.500000000000000000e-01,
3.506800000000000195e-02,
7.047809999999999908e-01,
1.097736000000000045e00,
-2.143069999999999975e-01,
5.321300000000000335e-02,
],
[
0.000000000000000000e00,
8.000000000000000444e-01,
1.168499999999999921e-02,
2.196390000000000009e-01,
3.197160000000000002e-01,
-8.798200000000000465e-02,
-1.926999999999999894e-02,
],
[
1.250000000000000000e00,
8.000000000000000444e-01,
1.481599999999999931e-02,
3.553939999999999877e-01,
5.435950000000000504e-01,
-1.317419999999999980e-01,
-1.345599999999999921e-02,
],
[
2.500000000000000000e00,
8.000000000000000444e-01,
1.968999999999999917e-02,
4.918299999999999894e-01,
7.669930000000000359e-01,
-1.728079999999999894e-01,
3.756999999999999923e-03,
],
[
3.750000000000000000e00,
8.000000000000000444e-01,
2.785599999999999882e-02,
6.324319999999999942e-01,
9.919249999999999456e-01,
-2.077100000000000057e-01,
3.159800000000000109e-02,
],
[
5.000000000000000000e00,
8.000000000000000444e-01,
4.394300000000000289e-02,
7.650689999999999991e-01,
1.188355999999999968e00,
-2.332680000000000031e-01,
5.645000000000000018e-02,
],
[
0.000000000000000000e00,
8.299999999999999600e-01,
1.186100000000000002e-02,
2.232899999999999885e-01,
3.261100000000000110e-01,
-9.028400000000000314e-02,
-1.806500000000000120e-02,
],
[
1.000000000000000000e00,
8.299999999999999600e-01,
1.444900000000000004e-02,
3.383419999999999761e-01,
5.161710000000000464e-01,
-1.279530000000000112e-01,
-1.402400000000000001e-02,
],
[
2.000000000000000000e00,
8.299999999999999600e-01,
1.836799999999999891e-02,
4.554270000000000262e-01,
7.082190000000000429e-01,
-1.642339999999999911e-01,
-1.793000000000000106e-03,
],
[
3.000000000000000000e00,
8.299999999999999600e-01,
2.466899999999999996e-02,
5.798410000000000508e-01,
9.088819999999999677e-01,
-2.004589999999999983e-01,
1.892900000000000138e-02,
],
[
4.000000000000000000e00,
8.299999999999999600e-01,
3.700400000000000217e-02,
7.012720000000000065e-01,
1.097366000000000064e00,
-2.362420000000000075e-01,
3.750699999999999867e-02,
],
[
0.000000000000000000e00,
8.599999999999999867e-01,
1.224300000000000041e-02,
2.278100000000000125e-01,
3.342720000000000136e-01,
-9.307600000000000595e-02,
-1.608400000000000107e-02,
],
[
1.000000000000000000e00,
8.599999999999999867e-01,
1.540700000000000056e-02,
3.551839999999999997e-01,
5.433130000000000459e-01,
-1.364730000000000110e-01,
-1.162200000000000039e-02,
],
[
2.000000000000000000e00,
8.599999999999999867e-01,
2.122699999999999934e-02,
4.854620000000000046e-01,
7.552919999999999634e-01,
-1.817850000000000021e-01,
1.070999999999999903e-03,
],
[
3.000000000000000000e00,
8.599999999999999867e-01,
3.178899999999999781e-02,
6.081849999999999756e-01,
9.510380000000000500e-01,
-2.252020000000000133e-01,
1.540799999999999982e-02,
],
[
4.000000000000000000e00,
8.599999999999999867e-01,
4.744199999999999806e-02,
6.846989999999999466e-01,
1.042564000000000046e00,
-2.333600000000000119e-01,
2.035400000000000056e-02,
],
]
)
def get_rans_crm_wing():
# data structure:
# alpha, mach, cd, cl, cmx, cmy, cmz
deg2rad = np.pi / 180.0
xt = np.array(raw[:, 0:2])
yt = np.array(raw[:, 2:4])
xlimits = np.array([[-3.0, 10.0], [0.4, 0.90]])
xt[:, 0] *= deg2rad
xlimits[0, :] *= deg2rad
return xt, yt, xlimits
def plot_rans_crm_wing(xt, yt, limits, interp):
import matplotlib
import numpy as np
matplotlib.use("Agg")
import matplotlib.pyplot as plt
rad2deg = 180.0 / np.pi
num = 500
num_a = 50
num_M = 50
x = np.zeros((num, 2))
colors = ["b", "g", "r", "c", "m", "k", "y"]
nrow = 3
ncol = 2
plt.close()
fig, axs = plt.subplots(nrow, ncol, figsize=(15, 15))
# -----------------------------------------------------------------------------
mach_numbers = [0.45, 0.68, 0.80, 0.86]
legend_entries = []
alpha_sweep = np.linspace(0.0, 8.0, num)
for ind, mach in enumerate(mach_numbers):
x[:, 0] = alpha_sweep / rad2deg
x[:, 1] = mach
CD = interp.predict_values(x)[:, 0]
CL = interp.predict_values(x)[:, 1]
mask = np.abs(xt[:, 1] - mach) < 1e-10
axs[0, 0].plot(xt[mask, 0] * rad2deg, yt[mask, 0], "o" + colors[ind])
axs[0, 0].plot(alpha_sweep, CD, colors[ind])
mask = np.abs(xt[:, 1] - mach) < 1e-10
axs[0, 1].plot(xt[mask, 0] * rad2deg, yt[mask, 1], "o" + colors[ind])
axs[0, 1].plot(alpha_sweep, CL, colors[ind])
legend_entries.append("M={}".format(mach))
legend_entries.append("exact")
axs[0, 0].set(xlabel="alpha (deg)", ylabel="CD")
axs[0, 0].legend(legend_entries)
axs[0, 1].set(xlabel="alpha (deg)", ylabel="CL")
axs[0, 1].legend(legend_entries)
# -----------------------------------------------------------------------------
alphas = [2.0, 4.0, 6.0]
legend_entries = []
mach_sweep = np.linspace(0.45, 0.86, num)
for ind, alpha in enumerate(alphas):
x[:, 0] = alpha / rad2deg
x[:, 1] = mach_sweep
CD = interp.predict_values(x)[:, 0]
CL = interp.predict_values(x)[:, 1]
axs[1, 0].plot(mach_sweep, CD, colors[ind])
axs[1, 1].plot(mach_sweep, CL, colors[ind])
legend_entries.append("alpha={}".format(alpha))
axs[1, 0].set(xlabel="Mach number", ylabel="CD")
axs[1, 0].legend(legend_entries)
axs[1, 1].set(xlabel="Mach number", ylabel="CL")
axs[1, 1].legend(legend_entries)
# -----------------------------------------------------------------------------
x = np.zeros((num_a, num_M, 2))
x[:, :, 0] = np.outer(np.linspace(0.0, 8.0, num_a), np.ones(num_M)) / rad2deg
x[:, :, 1] = np.outer(np.ones(num_a), np.linspace(0.45, 0.86, num_M))
CD = interp.predict_values(x.reshape((num_a * num_M, 2)))[:, 0].reshape(
(num_a, num_M)
)
CL = interp.predict_values(x.reshape((num_a * num_M, 2)))[:, 1].reshape(
(num_a, num_M)
)
axs[2, 0].plot(xt[:, 1], xt[:, 0] * rad2deg, "o")
axs[2, 0].contour(x[:, :, 1], x[:, :, 0] * rad2deg, CD, 20)
pcm1 = axs[2, 0].pcolormesh(
x[:, :, 1],
x[:, :, 0] * rad2deg,
CD,
cmap=plt.get_cmap("rainbow"),
shading="auto",
)
fig.colorbar(pcm1, ax=axs[2, 0])
axs[2, 0].set(xlabel="Mach number", ylabel="alpha (deg)")
axs[2, 0].set_title("CD")
axs[2, 1].plot(xt[:, 1], xt[:, 0] * rad2deg, "o")
axs[2, 1].contour(x[:, :, 1], x[:, :, 0] * rad2deg, CL, 20)
pcm2 = axs[2, 1].pcolormesh(
x[:, :, 1],
x[:, :, 0] * rad2deg,
CL,
cmap=plt.get_cmap("rainbow"),
shading="auto",
)
fig.colorbar(pcm2, ax=axs[2, 1])
axs[2, 1].set(xlabel="Mach number", ylabel="alpha (deg)")
axs[2, 1].set_title("CL")
plt.show()
RMTB¶
from smt.examples.rans_crm_wing.rans_crm_wing import (
get_rans_crm_wing,
plot_rans_crm_wing,
)
from smt.surrogate_models import RMTB
xt, yt, xlimits = get_rans_crm_wing()
interp = RMTB(
num_ctrl_pts=20, xlimits=xlimits, nonlinear_maxiter=100, energy_weight=1e-12
)
interp.set_training_values(xt, yt)
interp.train()
plot_rans_crm_wing(xt, yt, xlimits, interp)
___________________________________________________________________________
RMTB
___________________________________________________________________________
Problem size
# training points. : 35
___________________________________________________________________________
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): 0.0039933
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0000000
Pre-computing matrices - done. Time (sec): 0.0039933
Solving for degrees of freedom ...
Solving initial startup problem (n=400) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 9.429150220e-02 1.114942861e-02
Iteration (num., iy, grad. norm, func.) : 0 0 6.229432982e-09 1.793038469e-10
Solving for output 0 - done. Time (sec): 0.0055189
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.955493282e+00 4.799845498e+00
Iteration (num., iy, grad. norm, func.) : 0 1 2.880003477e-07 4.567660731e-08
Solving for output 1 - done. Time (sec): 0.0049999
Solving initial startup problem (n=400) - done. Time (sec): 0.0105188
Solving nonlinear problem (n=400) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 6.652713506e-09 1.793037943e-10
Iteration (num., iy, grad. norm, func.) : 0 0 5.849717047e-09 1.703954640e-10
Iteration (num., iy, grad. norm, func.) : 1 0 3.028427146e-08 1.034023971e-10
Iteration (num., iy, grad. norm, func.) : 2 0 1.125871064e-08 2.505156303e-11
Iteration (num., iy, grad. norm, func.) : 3 0 3.657654439e-09 1.062711951e-11
Iteration (num., iy, grad. norm, func.) : 4 0 2.384395507e-09 9.420573075e-12
Iteration (num., iy, grad. norm, func.) : 5 0 6.795760320e-10 7.398469916e-12
Iteration (num., iy, grad. norm, func.) : 6 0 1.893580345e-10 6.530836833e-12
Iteration (num., iy, grad. norm, func.) : 7 0 3.914693345e-11 6.262121148e-12
Iteration (num., iy, grad. norm, func.) : 8 0 2.607684883e-11 6.261389686e-12
Iteration (num., iy, grad. norm, func.) : 9 0 1.596473148e-11 6.260468564e-12
Iteration (num., iy, grad. norm, func.) : 10 0 8.775713015e-12 6.260124667e-12
Iteration (num., iy, grad. norm, func.) : 11 0 3.358656828e-12 6.256629410e-12
Iteration (num., iy, grad. norm, func.) : 12 0 5.210392100e-13 6.255688670e-12
Solving for output 0 - done. Time (sec): 0.0631311
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 9.729377248e-08 4.567642993e-08
Iteration (num., iy, grad. norm, func.) : 0 1 9.338363991e-08 4.538219456e-08
Iteration (num., iy, grad. norm, func.) : 1 1 2.891391770e-06 3.242337200e-08
Iteration (num., iy, grad. norm, func.) : 2 1 8.605107278e-07 4.652778039e-09
Iteration (num., iy, grad. norm, func.) : 3 1 5.067375622e-07 2.745465684e-09
Iteration (num., iy, grad. norm, func.) : 4 1 4.513840269e-07 2.455226693e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.339167789e-07 6.881109113e-10
Iteration (num., iy, grad. norm, func.) : 6 1 6.591489027e-08 5.500475184e-10
Iteration (num., iy, grad. norm, func.) : 7 1 3.839790426e-08 4.862409238e-10
Iteration (num., iy, grad. norm, func.) : 8 1 1.327787768e-08 4.053646260e-10
Iteration (num., iy, grad. norm, func.) : 9 1 4.178809432e-09 3.129533457e-10
Iteration (num., iy, grad. norm, func.) : 10 1 1.162280444e-09 2.724104537e-10
Iteration (num., iy, grad. norm, func.) : 11 1 9.071092861e-10 2.722854123e-10
Iteration (num., iy, grad. norm, func.) : 12 1 5.267790369e-10 2.722199641e-10
Iteration (num., iy, grad. norm, func.) : 13 1 3.128933500e-10 2.721466104e-10
Iteration (num., iy, grad. norm, func.) : 14 1 1.223449033e-10 2.716612344e-10
Iteration (num., iy, grad. norm, func.) : 15 1 3.662769998e-11 2.714261096e-10
Iteration (num., iy, grad. norm, func.) : 16 1 1.874681541e-11 2.713872039e-10
Iteration (num., iy, grad. norm, func.) : 17 1 4.264650696e-11 2.713834397e-10
Iteration (num., iy, grad. norm, func.) : 18 1 9.492032166e-12 2.713607454e-10
Iteration (num., iy, grad. norm, func.) : 19 1 1.706167514e-11 2.713578816e-10
Iteration (num., iy, grad. norm, func.) : 20 1 7.138581938e-12 2.713509077e-10
Iteration (num., iy, grad. norm, func.) : 21 1 8.869098799e-12 2.713470556e-10
Iteration (num., iy, grad. norm, func.) : 22 1 7.413215243e-12 2.713459004e-10
Iteration (num., iy, grad. norm, func.) : 23 1 2.698341151e-12 2.713454840e-10
Iteration (num., iy, grad. norm, func.) : 24 1 2.260359199e-12 2.713454159e-10
Iteration (num., iy, grad. norm, func.) : 25 1 2.987484747e-12 2.713452000e-10
Iteration (num., iy, grad. norm, func.) : 26 1 9.299976611e-13 2.713450184e-10
Solving for output 1 - done. Time (sec): 0.1322930
Solving nonlinear problem (n=400) - done. Time (sec): 0.1954241
Solving for degrees of freedom - done. Time (sec): 0.2059429
Training - done. Time (sec): 0.2109408
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0010037
Prediction time/pt. (sec) : 0.0000020
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0010016
Prediction time/pt. (sec) : 0.0000020
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0010087
Prediction time/pt. (sec) : 0.0000020
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0009997
Prediction time/pt. (sec) : 0.0000004
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0009985
Prediction time/pt. (sec) : 0.0000004
RMTC¶
from smt.examples.rans_crm_wing.rans_crm_wing import (
get_rans_crm_wing,
plot_rans_crm_wing,
)
from smt.surrogate_models import RMTC
xt, yt, xlimits = get_rans_crm_wing()
interp = RMTC(
num_elements=20, xlimits=xlimits, nonlinear_maxiter=100, energy_weight=1e-10
)
interp.set_training_values(xt, yt)
interp.train()
plot_rans_crm_wing(xt, yt, xlimits, interp)
___________________________________________________________________________
RMTC
___________________________________________________________________________
Problem size
# training points. : 35
___________________________________________________________________________
Training
Training ...
Pre-computing matrices ...
Computing dof2coeff ...
Computing dof2coeff - done. Time (sec): 0.0025086
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0000000
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.0080247
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0010026
Pre-computing matrices - done. Time (sec): 0.0115359
Solving for degrees of freedom ...
Solving initial startup problem (n=1764) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 1.279175539e-01 1.114942861e-02
Iteration (num., iy, grad. norm, func.) : 0 0 2.575575620e-06 2.207577304e-08
Solving for output 0 - done. Time (sec): 0.0190427
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 2.653045755e+00 4.799845498e+00
Iteration (num., iy, grad. norm, func.) : 0 1 5.806995269e-05 6.501953946e-06
Solving for output 1 - done. Time (sec): 0.0120237
Solving initial startup problem (n=1764) - done. Time (sec): 0.0310664
Solving nonlinear problem (n=1764) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 8.737858409e-07 2.207058354e-08
Iteration (num., iy, grad. norm, func.) : 0 0 9.583937937e-07 1.752349703e-08
Iteration (num., iy, grad. norm, func.) : 1 0 3.546737468e-07 3.273320789e-09
Iteration (num., iy, grad. norm, func.) : 2 0 1.183236974e-07 1.053100246e-09
Iteration (num., iy, grad. norm, func.) : 3 0 6.339993507e-08 5.347470320e-10
Iteration (num., iy, grad. norm, func.) : 4 0 3.374476763e-08 4.103939213e-10
Iteration (num., iy, grad. norm, func.) : 5 0 2.244269022e-08 3.752837377e-10
Iteration (num., iy, grad. norm, func.) : 6 0 1.965616160e-08 3.750955278e-10
Iteration (num., iy, grad. norm, func.) : 7 0 1.527792816e-08 3.661190229e-10
Iteration (num., iy, grad. norm, func.) : 8 0 1.694107204e-08 3.641575713e-10
Iteration (num., iy, grad. norm, func.) : 9 0 1.400350042e-08 3.400159769e-10
Iteration (num., iy, grad. norm, func.) : 10 0 8.618937187e-09 3.088359753e-10
Iteration (num., iy, grad. norm, func.) : 11 0 2.752861021e-09 2.905779707e-10
Iteration (num., iy, grad. norm, func.) : 12 0 2.459715986e-09 2.894122635e-10
Iteration (num., iy, grad. norm, func.) : 13 0 2.459715986e-09 2.894122635e-10
Iteration (num., iy, grad. norm, func.) : 14 0 2.459715986e-09 2.894122635e-10
Iteration (num., iy, grad. norm, func.) : 15 0 3.790841098e-09 2.884227179e-10
Iteration (num., iy, grad. norm, func.) : 16 0 7.660403189e-10 2.872925091e-10
Iteration (num., iy, grad. norm, func.) : 17 0 1.462954522e-09 2.870607852e-10
Iteration (num., iy, grad. norm, func.) : 18 0 9.736562709e-10 2.869808466e-10
Iteration (num., iy, grad. norm, func.) : 19 0 9.254952451e-10 2.869603286e-10
Iteration (num., iy, grad. norm, func.) : 20 0 8.664460782e-10 2.869027351e-10
Iteration (num., iy, grad. norm, func.) : 21 0 1.082182834e-09 2.867531901e-10
Iteration (num., iy, grad. norm, func.) : 22 0 7.203857332e-10 2.866272767e-10
Iteration (num., iy, grad. norm, func.) : 23 0 3.764133529e-10 2.865649123e-10
Iteration (num., iy, grad. norm, func.) : 24 0 3.282663853e-10 2.865624972e-10
Iteration (num., iy, grad. norm, func.) : 25 0 4.358374191e-10 2.865622292e-10
Iteration (num., iy, grad. norm, func.) : 26 0 4.203829589e-10 2.865539364e-10
Iteration (num., iy, grad. norm, func.) : 27 0 4.236541716e-10 2.865430468e-10
Iteration (num., iy, grad. norm, func.) : 28 0 2.549813945e-10 2.865278740e-10
Iteration (num., iy, grad. norm, func.) : 29 0 3.175902813e-10 2.865200228e-10
Iteration (num., iy, grad. norm, func.) : 30 0 1.818129781e-10 2.865131410e-10
Iteration (num., iy, grad. norm, func.) : 31 0 2.432582109e-10 2.865037093e-10
Iteration (num., iy, grad. norm, func.) : 32 0 8.721803489e-11 2.864965834e-10
Iteration (num., iy, grad. norm, func.) : 33 0 7.322984911e-11 2.864965493e-10
Iteration (num., iy, grad. norm, func.) : 34 0 8.732258237e-11 2.864961378e-10
Iteration (num., iy, grad. norm, func.) : 35 0 8.804680263e-11 2.864954282e-10
Iteration (num., iy, grad. norm, func.) : 36 0 8.914333813e-11 2.864951327e-10
Iteration (num., iy, grad. norm, func.) : 37 0 8.038605550e-11 2.864948918e-10
Iteration (num., iy, grad. norm, func.) : 38 0 1.211396047e-10 2.864945162e-10
Iteration (num., iy, grad. norm, func.) : 39 0 4.409752418e-11 2.864938221e-10
Iteration (num., iy, grad. norm, func.) : 40 0 5.330912907e-11 2.864934684e-10
Iteration (num., iy, grad. norm, func.) : 41 0 3.762907472e-11 2.864932279e-10
Iteration (num., iy, grad. norm, func.) : 42 0 5.711461398e-11 2.864930324e-10
Iteration (num., iy, grad. norm, func.) : 43 0 2.453269461e-11 2.864928261e-10
Iteration (num., iy, grad. norm, func.) : 44 0 3.736869281e-11 2.864928135e-10
Iteration (num., iy, grad. norm, func.) : 45 0 2.321967355e-11 2.864927721e-10
Iteration (num., iy, grad. norm, func.) : 46 0 3.910640430e-11 2.864927177e-10
Iteration (num., iy, grad. norm, func.) : 47 0 1.788968560e-11 2.864926239e-10
Iteration (num., iy, grad. norm, func.) : 48 0 2.002815893e-11 2.864925521e-10
Iteration (num., iy, grad. norm, func.) : 49 0 1.180675297e-11 2.864925052e-10
Iteration (num., iy, grad. norm, func.) : 50 0 1.941442472e-11 2.864925043e-10
Iteration (num., iy, grad. norm, func.) : 51 0 1.146499516e-11 2.864925028e-10
Iteration (num., iy, grad. norm, func.) : 52 0 1.650029728e-11 2.864924929e-10
Iteration (num., iy, grad. norm, func.) : 53 0 1.070168524e-11 2.864924715e-10
Iteration (num., iy, grad. norm, func.) : 54 0 1.177117044e-11 2.864924480e-10
Iteration (num., iy, grad. norm, func.) : 55 0 4.190805945e-12 2.864924309e-10
Iteration (num., iy, grad. norm, func.) : 56 0 3.466624617e-12 2.864924292e-10
Iteration (num., iy, grad. norm, func.) : 57 0 4.187491808e-12 2.864924274e-10
Iteration (num., iy, grad. norm, func.) : 58 0 4.908088917e-12 2.864924250e-10
Iteration (num., iy, grad. norm, func.) : 59 0 6.094730716e-12 2.864924232e-10
Iteration (num., iy, grad. norm, func.) : 60 0 3.555167666e-12 2.864924217e-10
Iteration (num., iy, grad. norm, func.) : 61 0 4.667159382e-12 2.864924216e-10
Iteration (num., iy, grad. norm, func.) : 62 0 2.758935157e-12 2.864924203e-10
Iteration (num., iy, grad. norm, func.) : 63 0 4.025897384e-12 2.864924191e-10
Iteration (num., iy, grad. norm, func.) : 64 0 1.886632298e-12 2.864924177e-10
Iteration (num., iy, grad. norm, func.) : 65 0 2.858509935e-12 2.864924172e-10
Iteration (num., iy, grad. norm, func.) : 66 0 1.505715565e-12 2.864924169e-10
Iteration (num., iy, grad. norm, func.) : 67 0 2.664590083e-12 2.864924165e-10
Iteration (num., iy, grad. norm, func.) : 68 0 8.712580952e-13 2.864924159e-10
Solving for output 0 - done. Time (sec): 0.9173832
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.434042246e-05 6.499348875e-06
Iteration (num., iy, grad. norm, func.) : 0 1 1.434144082e-05 6.252435785e-06
Iteration (num., iy, grad. norm, func.) : 1 1 1.476589235e-05 8.057263488e-07
Iteration (num., iy, grad. norm, func.) : 2 1 1.795902459e-05 3.606941390e-07
Iteration (num., iy, grad. norm, func.) : 3 1 5.530739183e-06 1.259947411e-07
Iteration (num., iy, grad. norm, func.) : 4 1 4.450520917e-06 9.727596048e-08
Iteration (num., iy, grad. norm, func.) : 5 1 1.368980438e-06 3.501563197e-08
Iteration (num., iy, grad. norm, func.) : 6 1 1.020853131e-06 3.008202531e-08
Iteration (num., iy, grad. norm, func.) : 7 1 7.823302514e-07 2.972410388e-08
Iteration (num., iy, grad. norm, func.) : 8 1 5.069636419e-07 2.921779973e-08
Iteration (num., iy, grad. norm, func.) : 9 1 1.871226190e-07 2.355740343e-08
Iteration (num., iy, grad. norm, func.) : 10 1 8.846817742e-08 1.806067121e-08
Iteration (num., iy, grad. norm, func.) : 11 1 4.191727220e-08 1.505842598e-08
Iteration (num., iy, grad. norm, func.) : 12 1 3.298523058e-08 1.477470833e-08
Iteration (num., iy, grad. norm, func.) : 13 1 3.298523058e-08 1.477470833e-08
Iteration (num., iy, grad. norm, func.) : 14 1 3.298523058e-08 1.477470833e-08
Iteration (num., iy, grad. norm, func.) : 15 1 3.527910738e-08 1.467890057e-08
Iteration (num., iy, grad. norm, func.) : 16 1 1.052194537e-08 1.453974951e-08
Iteration (num., iy, grad. norm, func.) : 17 1 1.462841687e-08 1.451934915e-08
Iteration (num., iy, grad. norm, func.) : 18 1 1.106621818e-08 1.450927248e-08
Iteration (num., iy, grad. norm, func.) : 19 1 1.458451265e-08 1.449957379e-08
Iteration (num., iy, grad. norm, func.) : 20 1 7.422220754e-09 1.449817301e-08
Iteration (num., iy, grad. norm, func.) : 21 1 1.266731481e-08 1.449629744e-08
Iteration (num., iy, grad. norm, func.) : 22 1 4.873470821e-09 1.448291296e-08
Iteration (num., iy, grad. norm, func.) : 23 1 5.830688146e-09 1.447443516e-08
Iteration (num., iy, grad. norm, func.) : 24 1 2.702817368e-09 1.446930180e-08
Iteration (num., iy, grad. norm, func.) : 25 1 3.482517704e-09 1.446853284e-08
Iteration (num., iy, grad. norm, func.) : 26 1 2.425197299e-09 1.446779033e-08
Iteration (num., iy, grad. norm, func.) : 27 1 4.384364765e-09 1.446656530e-08
Iteration (num., iy, grad. norm, func.) : 28 1 1.666807655e-09 1.446520269e-08
Iteration (num., iy, grad. norm, func.) : 29 1 1.146121195e-09 1.446505644e-08
Iteration (num., iy, grad. norm, func.) : 30 1 1.957016230e-09 1.446485007e-08
Iteration (num., iy, grad. norm, func.) : 31 1 1.400902411e-09 1.446446069e-08
Iteration (num., iy, grad. norm, func.) : 32 1 2.024876704e-09 1.446420260e-08
Iteration (num., iy, grad. norm, func.) : 33 1 7.392307757e-10 1.446402715e-08
Iteration (num., iy, grad. norm, func.) : 34 1 6.113871401e-10 1.446396613e-08
Iteration (num., iy, grad. norm, func.) : 35 1 8.897950108e-10 1.446387799e-08
Iteration (num., iy, grad. norm, func.) : 36 1 8.435572508e-10 1.446376880e-08
Iteration (num., iy, grad. norm, func.) : 37 1 6.781034056e-10 1.446369127e-08
Iteration (num., iy, grad. norm, func.) : 38 1 4.359785203e-10 1.446365605e-08
Iteration (num., iy, grad. norm, func.) : 39 1 3.604168165e-10 1.446364595e-08
Iteration (num., iy, grad. norm, func.) : 40 1 4.714920931e-10 1.446363720e-08
Iteration (num., iy, grad. norm, func.) : 41 1 4.111309595e-10 1.446361859e-08
Iteration (num., iy, grad. norm, func.) : 42 1 2.935425518e-10 1.446360051e-08
Iteration (num., iy, grad. norm, func.) : 43 1 4.007380257e-10 1.446358478e-08
Iteration (num., iy, grad. norm, func.) : 44 1 1.363139815e-10 1.446357223e-08
Iteration (num., iy, grad. norm, func.) : 45 1 9.870853160e-11 1.446357135e-08
Iteration (num., iy, grad. norm, func.) : 46 1 1.398078199e-10 1.446357010e-08
Iteration (num., iy, grad. norm, func.) : 47 1 1.301007213e-10 1.446356763e-08
Iteration (num., iy, grad. norm, func.) : 48 1 1.641898408e-10 1.446356566e-08
Iteration (num., iy, grad. norm, func.) : 49 1 1.162937331e-10 1.446356439e-08
Iteration (num., iy, grad. norm, func.) : 50 1 1.192123410e-10 1.446356022e-08
Iteration (num., iy, grad. norm, func.) : 51 1 6.556032628e-11 1.446355959e-08
Iteration (num., iy, grad. norm, func.) : 52 1 6.137545986e-11 1.446355959e-08
Iteration (num., iy, grad. norm, func.) : 53 1 4.468566796e-11 1.446355957e-08
Iteration (num., iy, grad. norm, func.) : 54 1 2.996918496e-11 1.446355952e-08
Iteration (num., iy, grad. norm, func.) : 55 1 1.980844385e-11 1.446355940e-08
Iteration (num., iy, grad. norm, func.) : 56 1 1.794802120e-11 1.446355928e-08
Iteration (num., iy, grad. norm, func.) : 57 1 9.147072506e-12 1.446355920e-08
Iteration (num., iy, grad. norm, func.) : 58 1 1.229585725e-11 1.446355919e-08
Iteration (num., iy, grad. norm, func.) : 59 1 9.365521643e-12 1.446355919e-08
Iteration (num., iy, grad. norm, func.) : 60 1 1.406243787e-11 1.446355918e-08
Iteration (num., iy, grad. norm, func.) : 61 1 5.522603866e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 62 1 6.529863254e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 63 1 4.754418096e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 64 1 6.124393750e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 65 1 6.089857348e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 66 1 1.636095665e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 67 1 1.518616253e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 68 1 2.823435682e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 69 1 1.718325361e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 70 1 1.805099003e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 71 1 1.353303475e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 72 1 1.740106987e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 73 1 4.164410037e-13 1.446355915e-08
Solving for output 1 - done. Time (sec): 0.9746635
Solving nonlinear problem (n=1764) - done. Time (sec): 1.8920467
Solving for degrees of freedom - done. Time (sec): 1.9231131
Training - done. Time (sec): 1.9346490
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0009995
Prediction time/pt. (sec) : 0.0000020
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0009997
Prediction time/pt. (sec) : 0.0000020
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0009987
Prediction time/pt. (sec) : 0.0000020
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0019970
Prediction time/pt. (sec) : 0.0000008
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0020299
Prediction time/pt. (sec) : 0.0000008
