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): 0.0156271
Computing approximation terms ...
Computing approximation terms - done. Time (sec): 0.0000000
Pre-computing matrices - done. Time (sec): 0.0156271
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 2.984735241e-08 1.793055991e-10
Solving for output 0 - done. Time (sec): 0.0156212
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 8.972452140e-07 4.567718425e-08
Solving for output 1 - done. Time (sec): 0.0178726
Solving initial startup problem (n=400) - done. Time (sec): 0.0334938
Solving nonlinear problem (n=400) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 6.652507490e-09 1.793038784e-10
Iteration (num., iy, grad. norm, func.) : 0 0 5.849530864e-09 1.703953551e-10
Iteration (num., iy, grad. norm, func.) : 1 0 3.026119118e-08 1.033358002e-10
Iteration (num., iy, grad. norm, func.) : 2 0 1.124244132e-08 2.502763076e-11
Iteration (num., iy, grad. norm, func.) : 3 0 3.716624334e-09 1.069397055e-11
Iteration (num., iy, grad. norm, func.) : 4 0 2.156599976e-09 9.185410025e-12
Iteration (num., iy, grad. norm, func.) : 5 0 6.117374981e-10 7.356144369e-12
Iteration (num., iy, grad. norm, func.) : 6 0 1.634984192e-10 6.520846187e-12
Iteration (num., iy, grad. norm, func.) : 7 0 3.210245246e-11 6.261004148e-12
Iteration (num., iy, grad. norm, func.) : 8 0 2.549150186e-11 6.258506806e-12
Iteration (num., iy, grad. norm, func.) : 9 0 1.471638517e-11 6.257588712e-12
Iteration (num., iy, grad. norm, func.) : 10 0 1.133341700e-11 6.257511725e-12
Iteration (num., iy, grad. norm, func.) : 11 0 3.893195802e-12 6.256384410e-12
Iteration (num., iy, grad. norm, func.) : 12 0 1.130684894e-12 6.255762511e-12
Iteration (num., iy, grad. norm, func.) : 13 0 1.026910429e-12 6.255701028e-12
Iteration (num., iy, grad. norm, func.) : 14 0 1.584455272e-12 6.255682352e-12
Iteration (num., iy, grad. norm, func.) : 15 0 3.772463044e-13 6.255651149e-12
Solving for output 0 - done. Time (sec): 0.1675348
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 9.728856644e-08 4.567640473e-08
Iteration (num., iy, grad. norm, func.) : 0 1 9.337495225e-08 4.538210157e-08
Iteration (num., iy, grad. norm, func.) : 1 1 2.791948127e-06 3.155039266e-08
Iteration (num., iy, grad. norm, func.) : 2 1 8.275304944e-07 4.459920781e-09
Iteration (num., iy, grad. norm, func.) : 3 1 5.530192329e-07 3.938719023e-09
Iteration (num., iy, grad. norm, func.) : 4 1 4.625412309e-07 3.226437182e-09
Iteration (num., iy, grad. norm, func.) : 5 1 1.357185480e-07 8.960620698e-10
Iteration (num., iy, grad. norm, func.) : 6 1 7.657562967e-08 6.428994523e-10
Iteration (num., iy, grad. norm, func.) : 7 1 2.256133023e-08 5.304151602e-10
Iteration (num., iy, grad. norm, func.) : 8 1 2.413627023e-08 5.204364288e-10
Iteration (num., iy, grad. norm, func.) : 9 1 7.138188474e-09 3.460141827e-10
Iteration (num., iy, grad. norm, func.) : 10 1 6.442125413e-09 2.791291396e-10
Iteration (num., iy, grad. norm, func.) : 11 1 3.731819626e-09 2.760526944e-10
Iteration (num., iy, grad. norm, func.) : 12 1 2.144556373e-09 2.758387713e-10
Iteration (num., iy, grad. norm, func.) : 13 1 7.067737867e-10 2.756939462e-10
Iteration (num., iy, grad. norm, func.) : 14 1 3.897646943e-10 2.733207471e-10
Iteration (num., iy, grad. norm, func.) : 15 1 9.475544403e-11 2.716010809e-10
Iteration (num., iy, grad. norm, func.) : 16 1 5.093208713e-11 2.714252376e-10
Iteration (num., iy, grad. norm, func.) : 17 1 4.469206461e-11 2.714102694e-10
Iteration (num., iy, grad. norm, func.) : 18 1 1.479537369e-11 2.713622143e-10
Iteration (num., iy, grad. norm, func.) : 19 1 2.119881723e-11 2.713583303e-10
Iteration (num., iy, grad. norm, func.) : 20 1 2.255262866e-11 2.713553725e-10
Iteration (num., iy, grad. norm, func.) : 21 1 2.278207268e-11 2.713509621e-10
Iteration (num., iy, grad. norm, func.) : 22 1 4.970532514e-12 2.713471679e-10
Iteration (num., iy, grad. norm, func.) : 23 1 8.558323103e-12 2.713468622e-10
Iteration (num., iy, grad. norm, func.) : 24 1 4.430058647e-12 2.713458173e-10
Iteration (num., iy, grad. norm, func.) : 25 1 6.949979717e-12 2.713453628e-10
Iteration (num., iy, grad. norm, func.) : 26 1 1.909219009e-12 2.713451665e-10
Iteration (num., iy, grad. norm, func.) : 27 1 1.615904818e-12 2.713451132e-10
Iteration (num., iy, grad. norm, func.) : 28 1 1.430058429e-12 2.713450210e-10
Iteration (num., iy, grad. norm, func.) : 29 1 8.733556585e-13 2.713449614e-10
Solving for output 1 - done. Time (sec): 0.2545013
Solving nonlinear problem (n=400) - done. Time (sec): 0.4220362
Solving for degrees of freedom - done. Time (sec): 0.4555299
Training - done. Time (sec): 0.4711571
___________________________________________________________________________
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): 0.0000000
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
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): 0.0166361
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): 0.0166361
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 3.676209532e-06 2.207093656e-08
Solving for output 0 - done. Time (sec): 0.0156279
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 5.931882707e-05 6.501854582e-06
Solving for output 1 - done. Time (sec): 0.0338449
Solving initial startup problem (n=1764) - done. Time (sec): 0.0494728
Solving nonlinear problem (n=1764) ...
Solving for output 0 ...
Iteration (num., iy, grad. norm, func.) : 0 0 8.720952301e-07 2.206099886e-08
Iteration (num., iy, grad. norm, func.) : 0 0 9.573075853e-07 1.751682593e-08
Iteration (num., iy, grad. norm, func.) : 1 0 3.547416599e-07 3.272729330e-09
Iteration (num., iy, grad. norm, func.) : 2 0 1.182883368e-07 1.052930200e-09
Iteration (num., iy, grad. norm, func.) : 3 0 6.342484570e-08 5.347132081e-10
Iteration (num., iy, grad. norm, func.) : 4 0 3.376386183e-08 4.104178247e-10
Iteration (num., iy, grad. norm, func.) : 5 0 2.246568041e-08 3.753037203e-10
Iteration (num., iy, grad. norm, func.) : 6 0 1.966169575e-08 3.751149837e-10
Iteration (num., iy, grad. norm, func.) : 7 0 1.521902520e-08 3.661319419e-10
Iteration (num., iy, grad. norm, func.) : 8 0 1.697219384e-08 3.641704633e-10
Iteration (num., iy, grad. norm, func.) : 9 0 1.397282892e-08 3.400303102e-10
Iteration (num., iy, grad. norm, func.) : 10 0 8.820213268e-09 3.088343729e-10
Iteration (num., iy, grad. norm, func.) : 11 0 2.662753070e-09 2.905736583e-10
Iteration (num., iy, grad. norm, func.) : 12 0 2.187726215e-09 2.894175993e-10
Iteration (num., iy, grad. norm, func.) : 13 0 2.187726215e-09 2.894175993e-10
Iteration (num., iy, grad. norm, func.) : 14 0 2.187726215e-09 2.894175993e-10
Iteration (num., iy, grad. norm, func.) : 15 0 4.200601330e-09 2.884082916e-10
Iteration (num., iy, grad. norm, func.) : 16 0 6.941954104e-10 2.872761171e-10
Iteration (num., iy, grad. norm, func.) : 17 0 1.541077466e-09 2.870609956e-10
Iteration (num., iy, grad. norm, func.) : 18 0 1.023649448e-09 2.869921846e-10
Iteration (num., iy, grad. norm, func.) : 19 0 1.212807643e-09 2.869885007e-10
Iteration (num., iy, grad. norm, func.) : 20 0 8.839931392e-10 2.869758703e-10
Iteration (num., iy, grad. norm, func.) : 21 0 1.306730857e-09 2.868275046e-10
Iteration (num., iy, grad. norm, func.) : 22 0 4.704438219e-10 2.866869990e-10
Iteration (num., iy, grad. norm, func.) : 23 0 5.230973601e-10 2.866251543e-10
Iteration (num., iy, grad. norm, func.) : 24 0 5.664431546e-10 2.866043737e-10
Iteration (num., iy, grad. norm, func.) : 25 0 4.828142306e-10 2.865968778e-10
Iteration (num., iy, grad. norm, func.) : 26 0 5.802434115e-10 2.865861641e-10
Iteration (num., iy, grad. norm, func.) : 27 0 3.737201709e-10 2.865567794e-10
Iteration (num., iy, grad. norm, func.) : 28 0 3.858419016e-10 2.865426826e-10
Iteration (num., iy, grad. norm, func.) : 29 0 2.901589088e-10 2.865289028e-10
Iteration (num., iy, grad. norm, func.) : 30 0 3.940180990e-10 2.865189697e-10
Iteration (num., iy, grad. norm, func.) : 31 0 1.690143798e-10 2.865096559e-10
Iteration (num., iy, grad. norm, func.) : 32 0 1.242855464e-10 2.865072677e-10
Iteration (num., iy, grad. norm, func.) : 33 0 1.811663536e-10 2.865044243e-10
Iteration (num., iy, grad. norm, func.) : 34 0 1.534058465e-10 2.865018335e-10
Iteration (num., iy, grad. norm, func.) : 35 0 1.924068784e-10 2.865004108e-10
Iteration (num., iy, grad. norm, func.) : 36 0 1.332616182e-10 2.864992145e-10
Iteration (num., iy, grad. norm, func.) : 37 0 1.465297323e-10 2.864980040e-10
Iteration (num., iy, grad. norm, func.) : 38 0 1.059898490e-10 2.864964801e-10
Iteration (num., iy, grad. norm, func.) : 39 0 6.833524389e-11 2.864949969e-10
Iteration (num., iy, grad. norm, func.) : 40 0 7.187619523e-11 2.864946363e-10
Iteration (num., iy, grad. norm, func.) : 41 0 8.331579773e-11 2.864944346e-10
Iteration (num., iy, grad. norm, func.) : 42 0 1.027434435e-10 2.864938961e-10
Iteration (num., iy, grad. norm, func.) : 43 0 2.785843313e-11 2.864931900e-10
Iteration (num., iy, grad. norm, func.) : 44 0 4.587732601e-11 2.864931837e-10
Iteration (num., iy, grad. norm, func.) : 45 0 3.359533850e-11 2.864931000e-10
Iteration (num., iy, grad. norm, func.) : 46 0 6.378182023e-11 2.864929524e-10
Iteration (num., iy, grad. norm, func.) : 47 0 2.376264253e-11 2.864927539e-10
Iteration (num., iy, grad. norm, func.) : 48 0 2.825935022e-11 2.864925180e-10
Iteration (num., iy, grad. norm, func.) : 49 0 9.565705264e-12 2.864924909e-10
Iteration (num., iy, grad. norm, func.) : 50 0 9.565696711e-12 2.864924909e-10
Iteration (num., iy, grad. norm, func.) : 51 0 9.565696216e-12 2.864924909e-10
Iteration (num., iy, grad. norm, func.) : 52 0 1.321236786e-11 2.864924690e-10
Iteration (num., iy, grad. norm, func.) : 53 0 3.200038592e-12 2.864924347e-10
Iteration (num., iy, grad. norm, func.) : 54 0 4.408441668e-12 2.864924282e-10
Iteration (num., iy, grad. norm, func.) : 55 0 3.685080752e-12 2.864924262e-10
Iteration (num., iy, grad. norm, func.) : 56 0 5.781840079e-12 2.864924241e-10
Iteration (num., iy, grad. norm, func.) : 57 0 3.797616430e-12 2.864924225e-10
Iteration (num., iy, grad. norm, func.) : 58 0 3.375356538e-12 2.864924219e-10
Iteration (num., iy, grad. norm, func.) : 59 0 3.219911457e-12 2.864924211e-10
Iteration (num., iy, grad. norm, func.) : 60 0 3.239255276e-12 2.864924194e-10
Iteration (num., iy, grad. norm, func.) : 61 0 2.217174829e-12 2.864924181e-10
Iteration (num., iy, grad. norm, func.) : 62 0 1.963609045e-12 2.864924173e-10
Iteration (num., iy, grad. norm, func.) : 63 0 2.012378650e-12 2.864924172e-10
Iteration (num., iy, grad. norm, func.) : 64 0 1.904781413e-12 2.864924172e-10
Iteration (num., iy, grad. norm, func.) : 65 0 3.311811443e-12 2.864924162e-10
Iteration (num., iy, grad. norm, func.) : 66 0 4.109786374e-13 2.864924155e-10
Solving for output 0 - done. Time (sec): 1.3435118
Solving for output 1 ...
Iteration (num., iy, grad. norm, func.) : 0 1 1.433843609e-05 6.499190122e-06
Iteration (num., iy, grad. norm, func.) : 0 1 1.433915036e-05 6.252291412e-06
Iteration (num., iy, grad. norm, func.) : 1 1 1.477636849e-05 8.059381431e-07
Iteration (num., iy, grad. norm, func.) : 2 1 1.952541750e-05 3.823795195e-07
Iteration (num., iy, grad. norm, func.) : 3 1 5.882568493e-06 1.304962153e-07
Iteration (num., iy, grad. norm, func.) : 4 1 4.984583502e-06 1.036536000e-07
Iteration (num., iy, grad. norm, func.) : 5 1 1.536768676e-06 3.777779455e-08
Iteration (num., iy, grad. norm, func.) : 6 1 1.481834363e-06 3.211344234e-08
Iteration (num., iy, grad. norm, func.) : 7 1 1.135248844e-06 3.114497290e-08
Iteration (num., iy, grad. norm, func.) : 8 1 4.713119283e-07 3.086507410e-08
Iteration (num., iy, grad. norm, func.) : 9 1 1.724080326e-07 2.442765672e-08
Iteration (num., iy, grad. norm, func.) : 10 1 1.379805070e-07 1.867418150e-08
Iteration (num., iy, grad. norm, func.) : 11 1 3.448834100e-08 1.502932128e-08
Iteration (num., iy, grad. norm, func.) : 12 1 2.795611473e-08 1.473756837e-08
Iteration (num., iy, grad. norm, func.) : 13 1 2.779055044e-08 1.473754505e-08
Iteration (num., iy, grad. norm, func.) : 14 1 2.733312548e-08 1.473737843e-08
Iteration (num., iy, grad. norm, func.) : 15 1 3.340811292e-08 1.460437573e-08
Iteration (num., iy, grad. norm, func.) : 16 1 6.626498801e-09 1.449141612e-08
Iteration (num., iy, grad. norm, func.) : 17 1 6.372795316e-09 1.448986180e-08
Iteration (num., iy, grad. norm, func.) : 18 1 6.929167016e-09 1.448716836e-08
Iteration (num., iy, grad. norm, func.) : 19 1 7.274804838e-09 1.448420494e-08
Iteration (num., iy, grad. norm, func.) : 20 1 6.352089406e-09 1.448163589e-08
Iteration (num., iy, grad. norm, func.) : 21 1 9.972423160e-09 1.447591555e-08
Iteration (num., iy, grad. norm, func.) : 22 1 3.304064579e-09 1.447128338e-08
Iteration (num., iy, grad. norm, func.) : 23 1 5.841019525e-09 1.447072301e-08
Iteration (num., iy, grad. norm, func.) : 24 1 3.393827849e-09 1.446954458e-08
Iteration (num., iy, grad. norm, func.) : 25 1 5.071952469e-09 1.446824634e-08
Iteration (num., iy, grad. norm, func.) : 26 1 1.833014270e-09 1.446634336e-08
Iteration (num., iy, grad. norm, func.) : 27 1 2.578318428e-09 1.446596197e-08
Iteration (num., iy, grad. norm, func.) : 28 1 1.739541145e-09 1.446570654e-08
Iteration (num., iy, grad. norm, func.) : 29 1 3.299993919e-09 1.446528512e-08
Iteration (num., iy, grad. norm, func.) : 30 1 1.127306394e-09 1.446463680e-08
Iteration (num., iy, grad. norm, func.) : 31 1 1.593374769e-09 1.446424957e-08
Iteration (num., iy, grad. norm, func.) : 32 1 6.758050764e-10 1.446401646e-08
Iteration (num., iy, grad. norm, func.) : 33 1 7.726504616e-10 1.446396254e-08
Iteration (num., iy, grad. norm, func.) : 34 1 7.572925345e-10 1.446391812e-08
Iteration (num., iy, grad. norm, func.) : 35 1 1.283660448e-09 1.446384601e-08
Iteration (num., iy, grad. norm, func.) : 36 1 5.394268295e-10 1.446377167e-08
Iteration (num., iy, grad. norm, func.) : 37 1 7.452734477e-10 1.446371202e-08
Iteration (num., iy, grad. norm, func.) : 38 1 3.669013694e-10 1.446365827e-08
Iteration (num., iy, grad. norm, func.) : 39 1 8.262896904e-10 1.446361117e-08
Iteration (num., iy, grad. norm, func.) : 40 1 1.468229194e-10 1.446358692e-08
Iteration (num., iy, grad. norm, func.) : 41 1 1.058490893e-10 1.446358652e-08
Iteration (num., iy, grad. norm, func.) : 42 1 3.077890236e-10 1.446358444e-08
Iteration (num., iy, grad. norm, func.) : 43 1 1.530864773e-10 1.446357588e-08
Iteration (num., iy, grad. norm, func.) : 44 1 2.233487372e-10 1.446357055e-08
Iteration (num., iy, grad. norm, func.) : 45 1 1.072821261e-10 1.446356870e-08
Iteration (num., iy, grad. norm, func.) : 46 1 1.569858063e-10 1.446356792e-08
Iteration (num., iy, grad. norm, func.) : 47 1 8.002372946e-11 1.446356534e-08
Iteration (num., iy, grad. norm, func.) : 48 1 1.161997835e-10 1.446356372e-08
Iteration (num., iy, grad. norm, func.) : 49 1 5.406312319e-11 1.446356185e-08
Iteration (num., iy, grad. norm, func.) : 50 1 8.972085170e-11 1.446356112e-08
Iteration (num., iy, grad. norm, func.) : 51 1 4.637381040e-11 1.446356084e-08
Iteration (num., iy, grad. norm, func.) : 52 1 8.212169076e-11 1.446356071e-08
Iteration (num., iy, grad. norm, func.) : 53 1 3.865629403e-11 1.446356017e-08
Iteration (num., iy, grad. norm, func.) : 54 1 5.417993980e-11 1.446355996e-08
Iteration (num., iy, grad. norm, func.) : 55 1 2.747884329e-11 1.446355971e-08
Iteration (num., iy, grad. norm, func.) : 56 1 3.802715340e-11 1.446355954e-08
Iteration (num., iy, grad. norm, func.) : 57 1 1.973480070e-11 1.446355943e-08
Iteration (num., iy, grad. norm, func.) : 58 1 1.656900358e-11 1.446355939e-08
Iteration (num., iy, grad. norm, func.) : 59 1 2.083227248e-11 1.446355934e-08
Iteration (num., iy, grad. norm, func.) : 60 1 1.866621335e-11 1.446355929e-08
Iteration (num., iy, grad. norm, func.) : 61 1 1.548239675e-11 1.446355925e-08
Iteration (num., iy, grad. norm, func.) : 62 1 1.590006746e-11 1.446355920e-08
Iteration (num., iy, grad. norm, func.) : 63 1 7.464996577e-12 1.446355917e-08
Iteration (num., iy, grad. norm, func.) : 64 1 7.029534858e-12 1.446355917e-08
Iteration (num., iy, grad. norm, func.) : 65 1 7.635808483e-12 1.446355917e-08
Iteration (num., iy, grad. norm, func.) : 66 1 8.725141256e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 67 1 4.683846749e-12 1.446355916e-08
Iteration (num., iy, grad. norm, func.) : 68 1 5.446573952e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 69 1 5.379658352e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 70 1 2.634341766e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 71 1 5.631339248e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 72 1 2.582076096e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 73 1 2.859607680e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 74 1 1.390873167e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 75 1 1.372832255e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 76 1 1.039726926e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 77 1 1.656395559e-12 1.446355915e-08
Iteration (num., iy, grad. norm, func.) : 78 1 9.864571633e-13 1.446355915e-08
Solving for output 1 - done. Time (sec): 1.5652311
Solving nonlinear problem (n=1764) - done. Time (sec): 2.9087429
Solving for degrees of freedom - done. Time (sec): 2.9582157
Training - done. Time (sec): 2.9748518
___________________________________________________________________________
Evaluation
# eval points. : 500
Predicting ...
Predicting - done. Time (sec): 0.0010328
Prediction time/pt. (sec) : 0.0000021
___________________________________________________________________________
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): 0.0020578
Prediction time/pt. (sec) : 0.0000041
___________________________________________________________________________
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): 0.0000000
Prediction time/pt. (sec) : 0.0000000
___________________________________________________________________________
Evaluation
# eval points. : 2500
Predicting ...
Predicting - done. Time (sec): 0.0000000
Prediction time/pt. (sec) : 0.0000000
