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): 0.0050461
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0000000
Pre-computing matrices - done. Time (sec): 0.0050461
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 3.285344182e-08 1.793057271e-10
Solving for output 0 - done. Time (sec): 0.0046282
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 2.576015345e-07 4.567654000e-08
Solving for output 1 - done. Time (sec): 0.0101466
Solving initial startup problem (n=400) - done. Time (sec): 0.0147748
Solving nonlinear problem (n=400) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 6.652468783e-09 1.793036975e-10
Iteration (num., iy, grad. norm, func.) : 0 0 5.849359661e-09 1.703948671e-10
Iteration (num., iy, grad. norm, func.) : 1 0 3.024325657e-08 1.032975972e-10
Iteration (num., iy, grad. norm, func.) : 2 0 1.125952154e-08 2.504755353e-11
Iteration (num., iy, grad. norm, func.) : 3 0 3.703330543e-09 1.068567321e-11
Iteration (num., iy, grad. norm, func.) : 4 0 2.308909897e-09 9.336148495e-12
Iteration (num., iy, grad. norm, func.) : 5 0 6.563532482e-10 7.374595825e-12
Iteration (num., iy, grad. norm, func.) : 6 0 1.899965043e-10 6.526023195e-12
Iteration (num., iy, grad. norm, func.) : 7 0 3.754706496e-11 6.261587544e-12
Iteration (num., iy, grad. norm, func.) : 8 0 2.324496290e-11 6.261399259e-12
Iteration (num., iy, grad. norm, func.) : 9 0 1.605020424e-11 6.260532352e-12
Iteration (num., iy, grad. norm, func.) : 10 0 9.318527760e-12 6.260087609e-12
Iteration (num., iy, grad. norm, func.) : 11 0 3.152266674e-12 6.256581242e-12
Iteration (num., iy, grad. norm, func.) : 12 0 6.297025943e-13 6.255685336e-12
Solving for output 0 - done. Time (sec): 0.0898802
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 9.729427657e-08 4.567642346e-08
Iteration (num., iy, grad. norm, func.) : 0 1 9.338325576e-08 4.538217216e-08
Iteration (num., iy, grad. norm, func.) : 1 1 2.905713447e-06 3.252950315e-08
Iteration (num., iy, grad. norm, func.) : 2 1 8.633229768e-07 4.671460509e-09
Iteration (num., iy, grad. norm, func.) : 3 1 3.070729855e-07 2.348794793e-09
Iteration (num., iy, grad. norm, func.) : 4 1 2.500598178e-07 1.843742999e-09
Iteration (num., iy, grad. norm, func.) : 5 1 7.358885180e-08 6.117826659e-10
Iteration (num., iy, grad. norm, func.) : 6 1 2.131842881e-08 4.697487696e-10
Iteration (num., iy, grad. norm, func.) : 7 1 8.392367562e-09 4.379120867e-10
Iteration (num., iy, grad. norm, func.) : 8 1 1.441027672e-08 3.875329318e-10
Iteration (num., iy, grad. norm, func.) : 9 1 4.418732381e-09 2.976219461e-10
Iteration (num., iy, grad. norm, func.) : 10 1 1.162494175e-09 2.732104388e-10
Iteration (num., iy, grad. norm, func.) : 11 1 1.029882269e-09 2.731585062e-10
Iteration (num., iy, grad. norm, func.) : 12 1 5.996851815e-10 2.729944378e-10
Iteration (num., iy, grad. norm, func.) : 13 1 2.773575123e-10 2.720631775e-10
Iteration (num., iy, grad. norm, func.) : 14 1 4.839897428e-11 2.714806989e-10
Iteration (num., iy, grad. norm, func.) : 15 1 3.510264913e-11 2.714720312e-10
Iteration (num., iy, grad. norm, func.) : 16 1 4.275628954e-11 2.714584243e-10
Iteration (num., iy, grad. norm, func.) : 17 1 4.600758315e-11 2.714215071e-10
Iteration (num., iy, grad. norm, func.) : 18 1 3.755405225e-11 2.713917882e-10
Iteration (num., iy, grad. norm, func.) : 19 1 2.030607591e-11 2.713645513e-10
Iteration (num., iy, grad. norm, func.) : 20 1 2.354692086e-11 2.713542916e-10
Iteration (num., iy, grad. norm, func.) : 21 1 1.575101494e-11 2.713528375e-10
Iteration (num., iy, grad. norm, func.) : 22 1 1.050318251e-11 2.713503217e-10
Iteration (num., iy, grad. norm, func.) : 23 1 1.594812517e-11 2.713483733e-10
Iteration (num., iy, grad. norm, func.) : 24 1 3.895308868e-12 2.713460532e-10
Iteration (num., iy, grad. norm, func.) : 25 1 5.032359799e-12 2.713456491e-10
Iteration (num., iy, grad. norm, func.) : 26 1 3.021954913e-12 2.713455900e-10
Iteration (num., iy, grad. norm, func.) : 27 1 4.520394294e-12 2.713453462e-10
Iteration (num., iy, grad. norm, func.) : 28 1 7.790196797e-13 2.713450408e-10
Solving for output 1 - done. Time (sec): 0.2023385
Solving nonlinear problem (n=400) - done. Time (sec): 0.2922187
Solving for degrees of freedom - done. Time (sec): 0.3069935
Training - done. Time (sec): 0.3120396
___________________________________________________________________________
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): 0.0066257
Prediction time/pt. (sec) : 0.0000133
___________________________________________________________________________
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. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0020466
Prediction time/pt. (sec) : 0.0000008
___________________________________________________________________________
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): 0.0020320
Initializing Hessian ...
Initializing Hessian - done. Time (sec): 0.0000000
Computing energy terms ...
Computing energy terms - done. Time (sec): 0.0080001
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0010014
Pre-computing matrices - done. Time (sec): 0.0110335
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 4.269613325e-06 2.206667028e-08
Solving for output 0 - done. Time (sec): 0.0160003
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 8.181842136e-05 6.499264090e-06
Solving for output 1 - done. Time (sec): 0.0116787
Solving initial startup problem (n=1764) - done. Time (sec): 0.0327158
Solving nonlinear problem (n=1764) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 8.706466102e-07 2.205254659e-08
Iteration (num., iy, grad. norm, func.) : 0 0 9.563774984e-07 1.751091936e-08
Iteration (num., iy, grad. norm, func.) : 1 0 3.540967624e-07 3.271690535e-09
Iteration (num., iy, grad. norm, func.) : 2 0 1.182287333e-07 1.052646339e-09
Iteration (num., iy, grad. norm, func.) : 3 0 6.338489775e-08 5.346409167e-10
Iteration (num., iy, grad. norm, func.) : 4 0 3.377551606e-08 4.104301514e-10
Iteration (num., iy, grad. norm, func.) : 5 0 2.242910012e-08 3.753259905e-10
Iteration (num., iy, grad. norm, func.) : 6 0 1.964970772e-08 3.751489728e-10
Iteration (num., iy, grad. norm, func.) : 7 0 1.527094421e-08 3.661593175e-10
Iteration (num., iy, grad. norm, func.) : 8 0 1.690202790e-08 3.641811225e-10
Iteration (num., iy, grad. norm, func.) : 9 0 1.404808000e-08 3.400408379e-10
Iteration (num., iy, grad. norm, func.) : 10 0 8.545659714e-09 3.088440096e-10
Iteration (num., iy, grad. norm, func.) : 11 0 2.764134834e-09 2.905853361e-10
Iteration (num., iy, grad. norm, func.) : 12 0 2.564977956e-09 2.894136297e-10
Iteration (num., iy, grad. norm, func.) : 13 0 2.564977954e-09 2.894136297e-10
Iteration (num., iy, grad. norm, func.) : 14 0 2.564977954e-09 2.894136297e-10
Iteration (num., iy, grad. norm, func.) : 15 0 3.829116565e-09 2.884285847e-10
Iteration (num., iy, grad. norm, func.) : 16 0 7.531911834e-10 2.872989730e-10
Iteration (num., iy, grad. norm, func.) : 17 0 1.467359380e-09 2.870606984e-10
Iteration (num., iy, grad. norm, func.) : 18 0 9.879625566e-10 2.869763841e-10
Iteration (num., iy, grad. norm, func.) : 19 0 9.122177076e-10 2.869563186e-10
Iteration (num., iy, grad. norm, func.) : 20 0 8.902786862e-10 2.869003688e-10
Iteration (num., iy, grad. norm, func.) : 21 0 1.135119850e-09 2.867529790e-10
Iteration (num., iy, grad. norm, func.) : 22 0 7.995220792e-10 2.866276258e-10
Iteration (num., iy, grad. norm, func.) : 23 0 3.328505805e-10 2.865652621e-10
Iteration (num., iy, grad. norm, func.) : 24 0 2.914967622e-10 2.865626286e-10
Iteration (num., iy, grad. norm, func.) : 25 0 4.202766941e-10 2.865616065e-10
Iteration (num., iy, grad. norm, func.) : 26 0 3.877343718e-10 2.865534583e-10
Iteration (num., iy, grad. norm, func.) : 27 0 4.306678978e-10 2.865437762e-10
Iteration (num., iy, grad. norm, func.) : 28 0 2.673682253e-10 2.865299886e-10
Iteration (num., iy, grad. norm, func.) : 29 0 3.275156607e-10 2.865207653e-10
Iteration (num., iy, grad. norm, func.) : 30 0 1.930712327e-10 2.865122795e-10
Iteration (num., iy, grad. norm, func.) : 31 0 2.110880200e-10 2.865087706e-10
Iteration (num., iy, grad. norm, func.) : 32 0 1.473132344e-10 2.865051960e-10
Iteration (num., iy, grad. norm, func.) : 33 0 1.942718886e-10 2.865041883e-10
Iteration (num., iy, grad. norm, func.) : 34 0 1.115813968e-10 2.865019571e-10
Iteration (num., iy, grad. norm, func.) : 35 0 1.662319465e-10 2.864991211e-10
Iteration (num., iy, grad. norm, func.) : 36 0 6.180638003e-11 2.864957999e-10
Iteration (num., iy, grad. norm, func.) : 37 0 1.062616250e-10 2.864956662e-10
Iteration (num., iy, grad. norm, func.) : 38 0 6.888942793e-11 2.864952745e-10
Iteration (num., iy, grad. norm, func.) : 39 0 1.155104075e-10 2.864948247e-10
Iteration (num., iy, grad. norm, func.) : 40 0 5.301847100e-11 2.864940891e-10
Iteration (num., iy, grad. norm, func.) : 41 0 6.018140313e-11 2.864937454e-10
Iteration (num., iy, grad. norm, func.) : 42 0 4.486986044e-11 2.864935284e-10
Iteration (num., iy, grad. norm, func.) : 43 0 4.403159581e-11 2.864932581e-10
Iteration (num., iy, grad. norm, func.) : 44 0 4.446981101e-11 2.864931203e-10
Iteration (num., iy, grad. norm, func.) : 45 0 3.306185597e-11 2.864929855e-10
Iteration (num., iy, grad. norm, func.) : 46 0 3.855294007e-11 2.864928687e-10
Iteration (num., iy, grad. norm, func.) : 47 0 2.386145287e-11 2.864927343e-10
Iteration (num., iy, grad. norm, func.) : 48 0 3.479120870e-11 2.864926795e-10
Iteration (num., iy, grad. norm, func.) : 49 0 2.038523321e-11 2.864926305e-10
Iteration (num., iy, grad. norm, func.) : 50 0 2.842194813e-11 2.864925264e-10
Iteration (num., iy, grad. norm, func.) : 51 0 5.869027097e-12 2.864924490e-10
Iteration (num., iy, grad. norm, func.) : 52 0 5.498904403e-12 2.864924489e-10
Iteration (num., iy, grad. norm, func.) : 53 0 7.306931689e-12 2.864924453e-10
Iteration (num., iy, grad. norm, func.) : 54 0 6.504376748e-12 2.864924392e-10
Iteration (num., iy, grad. norm, func.) : 55 0 1.045430092e-11 2.864924382e-10
Iteration (num., iy, grad. norm, func.) : 56 0 5.954310611e-12 2.864924378e-10
Iteration (num., iy, grad. norm, func.) : 57 0 8.350346501e-12 2.864924344e-10
Iteration (num., iy, grad. norm, func.) : 58 0 4.203044469e-12 2.864924276e-10
Iteration (num., iy, grad. norm, func.) : 59 0 4.808460418e-12 2.864924239e-10
Iteration (num., iy, grad. norm, func.) : 60 0 3.033848305e-12 2.864924214e-10
Iteration (num., iy, grad. norm, func.) : 61 0 4.617206475e-12 2.864924203e-10
Iteration (num., iy, grad. norm, func.) : 62 0 2.637312920e-12 2.864924192e-10
Iteration (num., iy, grad. norm, func.) : 63 0 3.304647023e-12 2.864924191e-10
Iteration (num., iy, grad. norm, func.) : 64 0 2.271894901e-12 2.864924185e-10
Iteration (num., iy, grad. norm, func.) : 65 0 3.579944006e-12 2.864924178e-10
Iteration (num., iy, grad. norm, func.) : 66 0 1.348728833e-12 2.864924169e-10
Iteration (num., iy, grad. norm, func.) : 67 0 1.806375611e-12 2.864924165e-10
Iteration (num., iy, grad. norm, func.) : 68 0 1.363756532e-12 2.864924163e-10
Iteration (num., iy, grad. norm, func.) : 69 0 1.712838050e-12 2.864924161e-10
Iteration (num., iy, grad. norm, func.) : 70 0 9.876379889e-13 2.864924158e-10
Solving for output 0 - done. Time (sec): 1.1035955
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.428317959e-05 6.494040493e-06
Iteration (num., iy, grad. norm, func.) : 0 1 1.428267378e-05 6.247373107e-06
Iteration (num., iy, grad. norm, func.) : 1 1 1.481274881e-05 8.062583973e-07
Iteration (num., iy, grad. norm, func.) : 2 1 1.867225216e-05 3.693188434e-07
Iteration (num., iy, grad. norm, func.) : 3 1 5.730360645e-06 1.284492940e-07
Iteration (num., iy, grad. norm, func.) : 4 1 4.881295024e-06 1.022046592e-07
Iteration (num., iy, grad. norm, func.) : 5 1 1.494072262e-06 3.710248943e-08
Iteration (num., iy, grad. norm, func.) : 6 1 1.213916647e-06 3.116830197e-08
Iteration (num., iy, grad. norm, func.) : 7 1 9.300654262e-07 3.027500084e-08
Iteration (num., iy, grad. norm, func.) : 8 1 3.309011543e-07 2.989622146e-08
Iteration (num., iy, grad. norm, func.) : 9 1 1.737272135e-07 2.372116544e-08
Iteration (num., iy, grad. norm, func.) : 10 1 1.192375978e-07 1.812151163e-08
Iteration (num., iy, grad. norm, func.) : 11 1 2.823830003e-08 1.492230509e-08
Iteration (num., iy, grad. norm, func.) : 12 1 3.075752735e-08 1.479543683e-08
Iteration (num., iy, grad. norm, func.) : 13 1 3.075752733e-08 1.479543683e-08
Iteration (num., iy, grad. norm, func.) : 14 1 3.075752733e-08 1.479543683e-08
Iteration (num., iy, grad. norm, func.) : 15 1 3.964583353e-08 1.465943906e-08
Iteration (num., iy, grad. norm, func.) : 16 1 7.803411275e-09 1.450888835e-08
Iteration (num., iy, grad. norm, func.) : 17 1 5.277665204e-09 1.449730293e-08
Iteration (num., iy, grad. norm, func.) : 18 1 1.430068303e-08 1.449498297e-08
Iteration (num., iy, grad. norm, func.) : 19 1 9.248180067e-09 1.449172013e-08
Iteration (num., iy, grad. norm, func.) : 20 1 9.340420132e-09 1.449165474e-08
Iteration (num., iy, grad. norm, func.) : 21 1 7.764038380e-09 1.448168658e-08
Iteration (num., iy, grad. norm, func.) : 22 1 6.859229342e-09 1.447218601e-08
Iteration (num., iy, grad. norm, func.) : 23 1 3.419486695e-09 1.446903012e-08
Iteration (num., iy, grad. norm, func.) : 24 1 3.133529225e-09 1.446840459e-08
Iteration (num., iy, grad. norm, func.) : 25 1 3.234821678e-09 1.446804176e-08
Iteration (num., iy, grad. norm, func.) : 26 1 4.311442242e-09 1.446751633e-08
Iteration (num., iy, grad. norm, func.) : 27 1 2.174738895e-09 1.446676165e-08
Iteration (num., iy, grad. norm, func.) : 28 1 3.665734460e-09 1.446612261e-08
Iteration (num., iy, grad. norm, func.) : 29 1 1.819294496e-09 1.446525702e-08
Iteration (num., iy, grad. norm, func.) : 30 1 1.688849698e-09 1.446466796e-08
Iteration (num., iy, grad. norm, func.) : 31 1 1.477487561e-09 1.446425046e-08
Iteration (num., iy, grad. norm, func.) : 32 1 1.048263967e-09 1.446400848e-08
Iteration (num., iy, grad. norm, func.) : 33 1 9.830692267e-10 1.446399517e-08
Iteration (num., iy, grad. norm, func.) : 34 1 1.112492217e-09 1.446398817e-08
Iteration (num., iy, grad. norm, func.) : 35 1 1.196589508e-09 1.446389015e-08
Iteration (num., iy, grad. norm, func.) : 36 1 9.757587962e-10 1.446374330e-08
Iteration (num., iy, grad. norm, func.) : 37 1 3.612625839e-10 1.446363742e-08
Iteration (num., iy, grad. norm, func.) : 38 1 3.212287016e-10 1.446362495e-08
Iteration (num., iy, grad. norm, func.) : 39 1 3.380688480e-10 1.446361644e-08
Iteration (num., iy, grad. norm, func.) : 40 1 4.451214840e-10 1.446360940e-08
Iteration (num., iy, grad. norm, func.) : 41 1 4.125923914e-10 1.446360055e-08
Iteration (num., iy, grad. norm, func.) : 42 1 3.316955216e-10 1.446358697e-08
Iteration (num., iy, grad. norm, func.) : 43 1 2.785079803e-10 1.446358341e-08
Iteration (num., iy, grad. norm, func.) : 44 1 2.210639567e-10 1.446357765e-08
Iteration (num., iy, grad. norm, func.) : 45 1 2.297590270e-10 1.446357276e-08
Iteration (num., iy, grad. norm, func.) : 46 1 1.366682844e-10 1.446356930e-08
Iteration (num., iy, grad. norm, func.) : 47 1 2.695700896e-10 1.446356498e-08
Iteration (num., iy, grad. norm, func.) : 48 1 4.755236155e-11 1.446356106e-08
Iteration (num., iy, grad. norm, func.) : 49 1 3.293668661e-11 1.446356098e-08
Iteration (num., iy, grad. norm, func.) : 50 1 6.150697758e-11 1.446356068e-08
Iteration (num., iy, grad. norm, func.) : 51 1 4.910616134e-11 1.446356031e-08
Iteration (num., iy, grad. norm, func.) : 52 1 6.141409525e-11 1.446355996e-08
Iteration (num., iy, grad. norm, func.) : 53 1 2.254141518e-11 1.446355962e-08
Iteration (num., iy, grad. norm, func.) : 54 1 2.548291847e-11 1.446355959e-08
Iteration (num., iy, grad. norm, func.) : 55 1 2.626783512e-11 1.446355952e-08
Iteration (num., iy, grad. norm, func.) : 56 1 2.951026223e-11 1.446355940e-08
Iteration (num., iy, grad. norm, func.) : 57 1 2.416840918e-11 1.446355932e-08
Iteration (num., iy, grad. norm, func.) : 58 1 1.583781374e-11 1.446355927e-08
Iteration (num., iy, grad. norm, func.) : 59 1 1.331983010e-11 1.446355926e-08
Iteration (num., iy, grad. norm, func.) : 60 1 1.538617826e-11 1.446355923e-08
Iteration (num., iy, grad. norm, func.) : 61 1 1.162017872e-11 1.446355920e-08
Iteration (num., iy, grad. norm, func.) : 62 1 1.308859447e-11 1.446355918e-08
Iteration (num., iy, grad. norm, func.) : 63 1 7.793346965e-12 1.446355918e-08
Iteration (num., iy, grad. norm, func.) : 64 1 1.252464249e-11 1.446355918e-08
Iteration (num., iy, grad. norm, func.) : 65 1 6.203040233e-12 1.446355917e-08
Iteration (num., iy, grad. norm, func.) : 66 1 6.342939419e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 67 1 4.780453656e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 68 1 4.876176332e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 69 1 2.643218436e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 70 1 2.173821409e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 71 1 3.103482666e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 72 1 3.133075055e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 73 1 2.316398650e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 74 1 2.928148144e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 75 1 1.225656672e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 76 1 1.460100479e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 77 1 1.400462776e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 78 1 1.694580342e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 79 1 8.560025835e-13 1.446355915e-08
Solving for output 1 - done. Time (sec): 1.2438643
Solving nonlinear problem (n=1764) - done. Time (sec): 2.3474598
Solving for degrees of freedom - done. Time (sec): 2.3801756
Training - done. Time (sec): 2.3912091
___________________________________________________________________________
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): 0.0010324
Prediction time/pt. (sec) : 0.0000021
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0009999
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): 0.0009999
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): 0.0009820
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): 0.0009930
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. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0020068
Prediction time/pt. (sec) : 0.0000008
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0019960
Prediction time/pt. (sec) : 0.0000008
