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 numpy as np
import matplotlib
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(3, 2, 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.surrogate_models import RMTB
from smt.examples.rans_crm_wing.rans_crm_wing import (
get_rans_crm_wing,
plot_rans_crm_wing,
)
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): 0.0000000
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0000000
Pre-computing matrices - done. Time (sec): 0.0000000
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 1.143986917e-08 1.793039631e-10
Solving for output 0 - done. Time (sec): 0.0000000
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 2.384072909e-06 4.568551517e-08
Solving for output 1 - done. Time (sec): 0.0156252
Solving initial startup problem (n=400) - done. Time (sec): 0.0156252
Solving nonlinear problem (n=400) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 6.652690767e-09 1.793037175e-10
Iteration (num., iy, grad. norm, func.) : 0 0 5.849579371e-09 1.703954904e-10
Iteration (num., iy, grad. norm, func.) : 1 0 3.029765479e-08 1.034424518e-10
Iteration (num., iy, grad. norm, func.) : 2 0 1.126327726e-08 2.505953287e-11
Iteration (num., iy, grad. norm, func.) : 3 0 3.684480315e-09 1.065597406e-11
Iteration (num., iy, grad. norm, func.) : 4 0 2.264648657e-09 9.297031284e-12
Iteration (num., iy, grad. norm, func.) : 5 0 6.433274344e-10 7.375855307e-12
Iteration (num., iy, grad. norm, func.) : 6 0 1.745403314e-10 6.524960110e-12
Iteration (num., iy, grad. norm, func.) : 7 0 3.515164760e-11 6.261432455e-12
Iteration (num., iy, grad. norm, func.) : 8 0 2.311171583e-11 6.261269938e-12
Iteration (num., iy, grad. norm, func.) : 9 0 1.659125824e-11 6.260501115e-12
Iteration (num., iy, grad. norm, func.) : 10 0 1.285972581e-11 6.260095232e-12
Iteration (num., iy, grad. norm, func.) : 11 0 2.948840801e-12 6.256556241e-12
Iteration (num., iy, grad. norm, func.) : 12 0 4.853416906e-13 6.255686534e-12
Solving for output 0 - done. Time (sec): 0.1002738
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 9.721474920e-08 4.567635024e-08
Iteration (num., iy, grad. norm, func.) : 0 1 9.329075021e-08 4.538184815e-08
Iteration (num., iy, grad. norm, func.) : 1 1 2.915771512e-06 3.263822593e-08
Iteration (num., iy, grad. norm, func.) : 2 1 8.640091715e-07 4.653851041e-09
Iteration (num., iy, grad. norm, func.) : 3 1 3.744485513e-07 2.548911362e-09
Iteration (num., iy, grad. norm, func.) : 4 1 3.391955543e-07 2.376502583e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.016715187e-07 7.621065834e-10
Iteration (num., iy, grad. norm, func.) : 6 1 2.973196096e-08 5.068032616e-10
Iteration (num., iy, grad. norm, func.) : 7 1 1.726322996e-08 4.692354715e-10
Iteration (num., iy, grad. norm, func.) : 8 1 5.115932969e-09 3.869684142e-10
Iteration (num., iy, grad. norm, func.) : 9 1 1.424825099e-09 2.978612739e-10
Iteration (num., iy, grad. norm, func.) : 10 1 3.388061716e-10 2.720847561e-10
Iteration (num., iy, grad. norm, func.) : 11 1 3.085067403e-10 2.720573550e-10
Iteration (num., iy, grad. norm, func.) : 12 1 1.850842452e-10 2.719821212e-10
Iteration (num., iy, grad. norm, func.) : 13 1 1.873073210e-10 2.717815229e-10
Iteration (num., iy, grad. norm, func.) : 14 1 2.846101886e-11 2.714550183e-10
Iteration (num., iy, grad. norm, func.) : 15 1 6.763872715e-11 2.714377475e-10
Iteration (num., iy, grad. norm, func.) : 16 1 2.942258822e-11 2.714091442e-10
Iteration (num., iy, grad. norm, func.) : 17 1 2.345315177e-11 2.713812224e-10
Iteration (num., iy, grad. norm, func.) : 18 1 7.043230003e-11 2.713685462e-10
Iteration (num., iy, grad. norm, func.) : 19 1 1.992995922e-11 2.713580756e-10
Iteration (num., iy, grad. norm, func.) : 20 1 7.780956057e-12 2.713512268e-10
Iteration (num., iy, grad. norm, func.) : 21 1 2.639523471e-11 2.713496139e-10
Iteration (num., iy, grad. norm, func.) : 22 1 7.530467475e-12 2.713478995e-10
Iteration (num., iy, grad. norm, func.) : 23 1 8.808167765e-12 2.713470106e-10
Iteration (num., iy, grad. norm, func.) : 24 1 3.650499393e-12 2.713457227e-10
Iteration (num., iy, grad. norm, func.) : 25 1 4.098006342e-12 2.713453909e-10
Iteration (num., iy, grad. norm, func.) : 26 1 2.122843484e-12 2.713452860e-10
Iteration (num., iy, grad. norm, func.) : 27 1 4.686426717e-12 2.713452133e-10
Iteration (num., iy, grad. norm, func.) : 28 1 7.792791774e-13 2.713450380e-10
Solving for output 1 - done. Time (sec): 0.1850064
Solving nonlinear problem (n=400) - done. Time (sec): 0.2852802
Solving for degrees of freedom - done. Time (sec): 0.3009055
Training - done. Time (sec): 0.3009055
___________________________________________________________________________
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): 0.0000000
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): 0.0156603
Prediction time/pt. (sec) : 0.0000313
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
RMTC¶
from smt.surrogate_models import RMTC
from smt.examples.rans_crm_wing.rans_crm_wing import (
get_rans_crm_wing,
plot_rans_crm_wing,
)
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): 0.0000000
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0000000
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.0155931
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0000000
Pre-computing matrices - done. Time (sec): 0.0155931
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 1.892260075e-05 2.158606140e-08
Solving for output 0 - done. Time (sec): 0.0156269
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 2.577030681e-04 6.438878057e-06
Solving for output 1 - done. Time (sec): 0.0156240
Solving initial startup problem (n=1764) - done. Time (sec): 0.0312510
Solving nonlinear problem (n=1764) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 7.702060163e-07 2.130719039e-08
Iteration (num., iy, grad. norm, func.) : 0 0 8.828496717e-07 1.695743786e-08
Iteration (num., iy, grad. norm, func.) : 1 0 3.480009880e-07 3.230274515e-09
Iteration (num., iy, grad. norm, func.) : 2 0 1.147855651e-07 1.039649991e-09
Iteration (num., iy, grad. norm, func.) : 3 0 6.174786868e-08 5.309471405e-10
Iteration (num., iy, grad. norm, func.) : 4 0 3.455593760e-08 4.118404972e-10
Iteration (num., iy, grad. norm, func.) : 5 0 2.266947966e-08 3.769645615e-10
Iteration (num., iy, grad. norm, func.) : 6 0 2.014457758e-08 3.726536483e-10
Iteration (num., iy, grad. norm, func.) : 7 0 2.062330624e-08 3.720494065e-10
Iteration (num., iy, grad. norm, func.) : 8 0 1.381296471e-08 3.609653111e-10
Iteration (num., iy, grad. norm, func.) : 9 0 1.506017697e-08 3.419689514e-10
Iteration (num., iy, grad. norm, func.) : 10 0 7.062492269e-09 3.064514419e-10
Iteration (num., iy, grad. norm, func.) : 11 0 2.380029319e-09 2.894188546e-10
Iteration (num., iy, grad. norm, func.) : 12 0 2.069095742e-09 2.893749129e-10
Iteration (num., iy, grad. norm, func.) : 13 0 3.378089318e-09 2.892327518e-10
Iteration (num., iy, grad. norm, func.) : 14 0 1.226570259e-09 2.876975386e-10
Iteration (num., iy, grad. norm, func.) : 15 0 1.422487571e-09 2.871370363e-10
Iteration (num., iy, grad. norm, func.) : 16 0 9.650973771e-10 2.870092820e-10
Iteration (num., iy, grad. norm, func.) : 17 0 1.293018053e-09 2.869726451e-10
Iteration (num., iy, grad. norm, func.) : 18 0 8.107957802e-10 2.869185409e-10
Iteration (num., iy, grad. norm, func.) : 19 0 1.313927988e-09 2.866658413e-10
Iteration (num., iy, grad. norm, func.) : 20 0 2.635834787e-10 2.865497160e-10
Iteration (num., iy, grad. norm, func.) : 21 0 2.635834734e-10 2.865497160e-10
Iteration (num., iy, grad. norm, func.) : 22 0 2.578774093e-10 2.865496490e-10
Iteration (num., iy, grad. norm, func.) : 23 0 4.117384437e-10 2.865446029e-10
Iteration (num., iy, grad. norm, func.) : 24 0 2.938743459e-10 2.865417065e-10
Iteration (num., iy, grad. norm, func.) : 25 0 5.384594569e-10 2.865323547e-10
Iteration (num., iy, grad. norm, func.) : 26 0 1.419599949e-10 2.865160822e-10
Iteration (num., iy, grad. norm, func.) : 27 0 2.269368628e-10 2.865093719e-10
Iteration (num., iy, grad. norm, func.) : 28 0 1.389025200e-10 2.865048956e-10
Iteration (num., iy, grad. norm, func.) : 29 0 1.707359787e-10 2.865042830e-10
Iteration (num., iy, grad. norm, func.) : 30 0 1.363224053e-10 2.865028080e-10
Iteration (num., iy, grad. norm, func.) : 31 0 2.464558404e-10 2.864983462e-10
Iteration (num., iy, grad. norm, func.) : 32 0 5.047775104e-11 2.864939811e-10
Iteration (num., iy, grad. norm, func.) : 33 0 3.304233461e-11 2.864939139e-10
Iteration (num., iy, grad. norm, func.) : 34 0 4.818707765e-11 2.864938652e-10
Iteration (num., iy, grad. norm, func.) : 35 0 4.519097374e-11 2.864937224e-10
Iteration (num., iy, grad. norm, func.) : 36 0 5.347794138e-11 2.864935884e-10
Iteration (num., iy, grad. norm, func.) : 37 0 9.066389563e-11 2.864933993e-10
Iteration (num., iy, grad. norm, func.) : 38 0 3.049731314e-11 2.864931818e-10
Iteration (num., iy, grad. norm, func.) : 39 0 3.563923657e-11 2.864930777e-10
Iteration (num., iy, grad. norm, func.) : 40 0 3.265928637e-11 2.864928292e-10
Iteration (num., iy, grad. norm, func.) : 41 0 1.841040766e-11 2.864925965e-10
Iteration (num., iy, grad. norm, func.) : 42 0 1.806812407e-11 2.864925807e-10
Iteration (num., iy, grad. norm, func.) : 43 0 2.420473432e-11 2.864925725e-10
Iteration (num., iy, grad. norm, func.) : 44 0 1.916950121e-11 2.864925455e-10
Iteration (num., iy, grad. norm, func.) : 45 0 1.328187605e-11 2.864925285e-10
Iteration (num., iy, grad. norm, func.) : 46 0 2.093336318e-11 2.864924982e-10
Iteration (num., iy, grad. norm, func.) : 47 0 8.582752113e-12 2.864924638e-10
Iteration (num., iy, grad. norm, func.) : 48 0 8.717555405e-12 2.864924505e-10
Iteration (num., iy, grad. norm, func.) : 49 0 6.296791425e-12 2.864924452e-10
Iteration (num., iy, grad. norm, func.) : 50 0 7.865314931e-12 2.864924377e-10
Iteration (num., iy, grad. norm, func.) : 51 0 7.544612204e-12 2.864924318e-10
Iteration (num., iy, grad. norm, func.) : 52 0 5.414373093e-12 2.864924301e-10
Iteration (num., iy, grad. norm, func.) : 53 0 6.886442439e-12 2.864924291e-10
Iteration (num., iy, grad. norm, func.) : 54 0 4.806737525e-12 2.864924252e-10
Iteration (num., iy, grad. norm, func.) : 55 0 4.786048698e-12 2.864924232e-10
Iteration (num., iy, grad. norm, func.) : 56 0 3.098569355e-12 2.864924211e-10
Iteration (num., iy, grad. norm, func.) : 57 0 3.094287700e-12 2.864924198e-10
Iteration (num., iy, grad. norm, func.) : 58 0 2.353142651e-12 2.864924186e-10
Iteration (num., iy, grad. norm, func.) : 59 0 2.921324161e-12 2.864924181e-10
Iteration (num., iy, grad. norm, func.) : 60 0 2.428313938e-12 2.864924176e-10
Iteration (num., iy, grad. norm, func.) : 61 0 2.471043088e-12 2.864924172e-10
Iteration (num., iy, grad. norm, func.) : 62 0 1.730575668e-12 2.864924167e-10
Iteration (num., iy, grad. norm, func.) : 63 0 1.508461037e-12 2.864924164e-10
Iteration (num., iy, grad. norm, func.) : 64 0 1.453987524e-12 2.864924162e-10
Iteration (num., iy, grad. norm, func.) : 65 0 1.684033544e-12 2.864924160e-10
Iteration (num., iy, grad. norm, func.) : 66 0 9.729856732e-13 2.864924158e-10
Solving for output 0 - done. Time (sec): 0.9712842
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.314155074e-05 6.384202420e-06
Iteration (num., iy, grad. norm, func.) : 0 1 1.315928341e-05 6.143977713e-06
Iteration (num., iy, grad. norm, func.) : 1 1 1.154682919e-05 7.656947029e-07
Iteration (num., iy, grad. norm, func.) : 2 1 1.465203371e-05 2.980622274e-07
Iteration (num., iy, grad. norm, func.) : 3 1 4.625767090e-06 1.079688836e-07
Iteration (num., iy, grad. norm, func.) : 4 1 8.246064892e-06 9.371523682e-08
Iteration (num., iy, grad. norm, func.) : 5 1 5.367834919e-06 6.438439692e-08
Iteration (num., iy, grad. norm, func.) : 6 1 1.544832966e-06 3.861049851e-08
Iteration (num., iy, grad. norm, func.) : 7 1 9.031989319e-07 3.389328335e-08
Iteration (num., iy, grad. norm, func.) : 8 1 3.999021991e-07 3.025673984e-08
Iteration (num., iy, grad. norm, func.) : 9 1 1.814510006e-07 2.271049772e-08
Iteration (num., iy, grad. norm, func.) : 10 1 8.858417326e-08 1.670709375e-08
Iteration (num., iy, grad. norm, func.) : 11 1 3.056416974e-08 1.464252693e-08
Iteration (num., iy, grad. norm, func.) : 12 1 2.782520357e-08 1.462325742e-08
Iteration (num., iy, grad. norm, func.) : 13 1 2.782520357e-08 1.462325742e-08
Iteration (num., iy, grad. norm, func.) : 14 1 2.727422124e-08 1.462029725e-08
Iteration (num., iy, grad. norm, func.) : 15 1 1.965107805e-08 1.459512623e-08
Iteration (num., iy, grad. norm, func.) : 16 1 2.096614317e-08 1.458538175e-08
Iteration (num., iy, grad. norm, func.) : 17 1 1.236092175e-08 1.454967165e-08
Iteration (num., iy, grad. norm, func.) : 18 1 1.593068541e-08 1.451034172e-08
Iteration (num., iy, grad. norm, func.) : 19 1 5.275613492e-09 1.448191845e-08
Iteration (num., iy, grad. norm, func.) : 20 1 8.440365910e-09 1.447727819e-08
Iteration (num., iy, grad. norm, func.) : 21 1 5.742131647e-09 1.447717235e-08
Iteration (num., iy, grad. norm, func.) : 22 1 9.954048290e-09 1.447641380e-08
Iteration (num., iy, grad. norm, func.) : 23 1 3.343603473e-09 1.447043541e-08
Iteration (num., iy, grad. norm, func.) : 24 1 4.464192152e-09 1.446947735e-08
Iteration (num., iy, grad. norm, func.) : 25 1 2.826027167e-09 1.446820216e-08
Iteration (num., iy, grad. norm, func.) : 26 1 4.161702182e-09 1.446691431e-08
Iteration (num., iy, grad. norm, func.) : 27 1 1.748053041e-09 1.446584543e-08
Iteration (num., iy, grad. norm, func.) : 28 1 2.845455738e-09 1.446523563e-08
Iteration (num., iy, grad. norm, func.) : 29 1 1.232116011e-09 1.446469173e-08
Iteration (num., iy, grad. norm, func.) : 30 1 1.086781065e-09 1.446455345e-08
Iteration (num., iy, grad. norm, func.) : 31 1 1.368466139e-09 1.446430399e-08
Iteration (num., iy, grad. norm, func.) : 32 1 1.055677821e-09 1.446403720e-08
Iteration (num., iy, grad. norm, func.) : 33 1 1.513493352e-09 1.446383873e-08
Iteration (num., iy, grad. norm, func.) : 34 1 5.201430031e-10 1.446375455e-08
Iteration (num., iy, grad. norm, func.) : 35 1 4.051375251e-10 1.446374741e-08
Iteration (num., iy, grad. norm, func.) : 36 1 6.626621516e-10 1.446372958e-08
Iteration (num., iy, grad. norm, func.) : 37 1 5.304259808e-10 1.446367918e-08
Iteration (num., iy, grad. norm, func.) : 38 1 3.647179408e-10 1.446362945e-08
Iteration (num., iy, grad. norm, func.) : 39 1 4.390649321e-10 1.446360112e-08
Iteration (num., iy, grad. norm, func.) : 40 1 2.551311266e-10 1.446359078e-08
Iteration (num., iy, grad. norm, func.) : 41 1 2.025727989e-10 1.446358914e-08
Iteration (num., iy, grad. norm, func.) : 42 1 2.590828580e-10 1.446358635e-08
Iteration (num., iy, grad. norm, func.) : 43 1 2.465075755e-10 1.446357854e-08
Iteration (num., iy, grad. norm, func.) : 44 1 1.708026086e-10 1.446357145e-08
Iteration (num., iy, grad. norm, func.) : 45 1 1.842322085e-10 1.446356736e-08
Iteration (num., iy, grad. norm, func.) : 46 1 1.123597500e-10 1.446356547e-08
Iteration (num., iy, grad. norm, func.) : 47 1 1.775927306e-10 1.446356518e-08
Iteration (num., iy, grad. norm, func.) : 48 1 9.881865974e-11 1.446356399e-08
Iteration (num., iy, grad. norm, func.) : 49 1 9.987164697e-11 1.446356318e-08
Iteration (num., iy, grad. norm, func.) : 50 1 7.129935940e-11 1.446356175e-08
Iteration (num., iy, grad. norm, func.) : 51 1 8.491078820e-11 1.446356074e-08
Iteration (num., iy, grad. norm, func.) : 52 1 3.521791020e-11 1.446356015e-08
Iteration (num., iy, grad. norm, func.) : 53 1 6.356842260e-11 1.446356014e-08
Iteration (num., iy, grad. norm, func.) : 54 1 4.199148446e-11 1.446356003e-08
Iteration (num., iy, grad. norm, func.) : 55 1 7.261550336e-11 1.446355987e-08
Iteration (num., iy, grad. norm, func.) : 56 1 2.400363005e-11 1.446355951e-08
Iteration (num., iy, grad. norm, func.) : 57 1 2.328511049e-11 1.446355939e-08
Iteration (num., iy, grad. norm, func.) : 58 1 2.043397073e-11 1.446355936e-08
Iteration (num., iy, grad. norm, func.) : 59 1 3.482645243e-11 1.446355935e-08
Iteration (num., iy, grad. norm, func.) : 60 1 1.573846518e-11 1.446355931e-08
Iteration (num., iy, grad. norm, func.) : 61 1 2.923943706e-11 1.446355925e-08
Iteration (num., iy, grad. norm, func.) : 62 1 7.795698296e-12 1.446355918e-08
Iteration (num., iy, grad. norm, func.) : 63 1 4.678331368e-12 1.446355917e-08
Iteration (num., iy, grad. norm, func.) : 64 1 6.784246138e-12 1.446355917e-08
Iteration (num., iy, grad. norm, func.) : 65 1 4.956253838e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 66 1 8.895340176e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 67 1 5.759073026e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 68 1 4.432107556e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 69 1 4.534868480e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 70 1 3.585348066e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 71 1 2.991273639e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 72 1 2.181563222e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 73 1 3.605945929e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 74 1 9.847431611e-13 1.446355915e-08
Solving for output 1 - done. Time (sec): 1.1418650
Solving nonlinear problem (n=1764) - done. Time (sec): 2.1131492
Solving for degrees of freedom - done. Time (sec): 2.1444001
Training - done. Time (sec): 2.1599932
___________________________________________________________________________
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): 0.0081306
Prediction time/pt. (sec) : 0.0000163
___________________________________________________________________________
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): 0.0080855
Prediction time/pt. (sec) : 0.0000162
___________________________________________________________________________
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): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
