RANS CRM wing 2-D data set ========================== .. code-block:: python 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 ---- .. code-block:: python 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): 0.0000000 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 6.361608377e-09 1.793038202e-10 Solving for output 0 - done. Time (sec): 0.0157151 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 1.413882548e-07 4.567643574e-08 Solving for output 1 - done. Time (sec): 0.0000000 Solving initial startup problem (n=400) - done. Time (sec): 0.0157151 Solving nonlinear problem (n=400) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 6.652712074e-09 1.793037678e-10 Iteration (num., iy, grad. norm, func.) : 0 0 5.849628222e-09 1.703954128e-10 Iteration (num., iy, grad. norm, func.) : 1 0 3.027427470e-08 1.033861402e-10 Iteration (num., iy, grad. norm, func.) : 2 0 1.125798382e-08 2.504937528e-11 Iteration (num., iy, grad. norm, func.) : 3 0 3.573484541e-09 1.053192602e-11 Iteration (num., iy, grad. norm, func.) : 4 0 2.491313135e-09 9.521725888e-12 Iteration (num., iy, grad. norm, func.) : 5 0 7.373265568e-10 7.437104352e-12 Iteration (num., iy, grad. norm, func.) : 6 0 2.127610441e-10 6.541104844e-12 Iteration (num., iy, grad. norm, func.) : 7 0 4.491224738e-11 6.262749389e-12 Iteration (num., iy, grad. norm, func.) : 8 0 2.678528584e-11 6.262036113e-12 Iteration (num., iy, grad. norm, func.) : 9 0 1.890458725e-11 6.260921588e-12 Iteration (num., iy, grad. norm, func.) : 10 0 1.026127463e-11 6.260205674e-12 Iteration (num., iy, grad. norm, func.) : 11 0 3.073950306e-12 6.256593531e-12 Iteration (num., iy, grad. norm, func.) : 12 0 6.628206941e-13 6.255688696e-12 Solving for output 0 - done. Time (sec): 0.0844629 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 9.729512355e-08 4.567641548e-08 Iteration (num., iy, grad. norm, func.) : 0 1 9.338535090e-08 4.538218375e-08 Iteration (num., iy, grad. norm, func.) : 1 1 2.793660470e-06 3.158625636e-08 Iteration (num., iy, grad. norm, func.) : 2 1 8.280829537e-07 4.467205623e-09 Iteration (num., iy, grad. norm, func.) : 3 1 2.471585317e-07 1.754067656e-09 Iteration (num., iy, grad. norm, func.) : 4 1 7.360944708e-08 7.514442525e-10 Iteration (num., iy, grad. norm, func.) : 5 1 6.382088695e-08 6.202555675e-10 Iteration (num., iy, grad. norm, func.) : 6 1 1.975747825e-08 5.727770136e-10 Iteration (num., iy, grad. norm, func.) : 7 1 6.305403118e-09 4.470752571e-10 Iteration (num., iy, grad. norm, func.) : 8 1 6.539592945e-09 3.117262610e-10 Iteration (num., iy, grad. norm, func.) : 9 1 1.800710465e-09 2.765831061e-10 Iteration (num., iy, grad. norm, func.) : 10 1 1.432583667e-09 2.762228885e-10 Iteration (num., iy, grad. norm, func.) : 11 1 3.459281962e-10 2.734946089e-10 Iteration (num., iy, grad. norm, func.) : 12 1 1.937089163e-10 2.719093375e-10 Iteration (num., iy, grad. norm, func.) : 13 1 4.191227557e-11 2.714583258e-10 Iteration (num., iy, grad. norm, func.) : 14 1 5.379716328e-11 2.714249977e-10 Iteration (num., iy, grad. norm, func.) : 15 1 3.735608154e-11 2.714059840e-10 Iteration (num., iy, grad. norm, func.) : 16 1 5.074917089e-11 2.713945463e-10 Iteration (num., iy, grad. norm, func.) : 17 1 1.135494957e-11 2.713592113e-10 Iteration (num., iy, grad. norm, func.) : 18 1 1.866777371e-11 2.713567409e-10 Iteration (num., iy, grad. norm, func.) : 19 1 1.365618643e-11 2.713544120e-10 Iteration (num., iy, grad. norm, func.) : 20 1 2.196197154e-11 2.713503041e-10 Iteration (num., iy, grad. norm, func.) : 21 1 4.703269102e-12 2.713473057e-10 Iteration (num., iy, grad. norm, func.) : 22 1 1.577064311e-11 2.713464813e-10 Iteration (num., iy, grad. norm, func.) : 23 1 3.126100067e-12 2.713459142e-10 Iteration (num., iy, grad. norm, func.) : 24 1 4.329925240e-12 2.713456898e-10 Iteration (num., iy, grad. norm, func.) : 25 1 4.760702705e-12 2.713452984e-10 Iteration (num., iy, grad. norm, func.) : 26 1 2.186298077e-12 2.713451820e-10 Iteration (num., iy, grad. norm, func.) : 27 1 1.090371663e-12 2.713450690e-10 Iteration (num., iy, grad. norm, func.) : 28 1 1.643853117e-12 2.713449846e-10 Iteration (num., iy, grad. norm, func.) : 29 1 9.754974651e-13 2.713449617e-10 Solving for output 1 - done. Time (sec): 0.2075577 Solving nonlinear problem (n=400) - done. Time (sec): 0.2920206 Solving for degrees of freedom - done. Time (sec): 0.3077357 Training - done. Time (sec): 0.3077357 ___________________________________________________________________________ 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): 0.0084314 Prediction time/pt. (sec) : 0.0000034 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 .. figure:: rans_crm_wing.png :scale: 60 % :align: center RMTC ---- .. code-block:: python 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): 0.0000000 Initializing Hessian ... Initializing Hessian - done. Time (sec): 0.0000000 Computing energy terms ... Computing energy terms - done. Time (sec): 0.0159726 Computing approximation terms ... Computing approximation terms - done. Time (sec): 0.0000000 Pre-computing matrices - done. Time (sec): 0.0159726 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 1.040702505e-05 2.104565751e-08 Solving for output 0 - done. Time (sec): 0.0156188 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 1.009043784e-04 6.496775939e-06 Solving for output 1 - done. Time (sec): 0.0156209 Solving initial startup problem (n=1764) - done. Time (sec): 0.0312397 Solving nonlinear problem (n=1764) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 8.424965568e-07 2.095596418e-08 Iteration (num., iy, grad. norm, func.) : 0 0 8.937561990e-07 1.660200956e-08 Iteration (num., iy, grad. norm, func.) : 1 0 3.417439495e-07 3.185554201e-09 Iteration (num., iy, grad. norm, func.) : 2 0 1.135240095e-07 1.027663890e-09 Iteration (num., iy, grad. norm, func.) : 3 0 6.040725703e-08 5.273037477e-10 Iteration (num., iy, grad. norm, func.) : 4 0 3.589817049e-08 4.130921956e-10 Iteration (num., iy, grad. norm, func.) : 5 0 2.367069222e-08 3.787308651e-10 Iteration (num., iy, grad. norm, func.) : 6 0 2.073063682e-08 3.750425098e-10 Iteration (num., iy, grad. norm, func.) : 7 0 2.151639102e-08 3.738627774e-10 Iteration (num., iy, grad. norm, func.) : 8 0 1.387810346e-08 3.612029122e-10 Iteration (num., iy, grad. norm, func.) : 9 0 1.599147620e-08 3.410626872e-10 Iteration (num., iy, grad. norm, func.) : 10 0 5.969921818e-09 3.050205235e-10 Iteration (num., iy, grad. norm, func.) : 11 0 2.143674691e-09 2.891346316e-10 Iteration (num., iy, grad. norm, func.) : 12 0 1.704740268e-09 2.876659596e-10 Iteration (num., iy, grad. norm, func.) : 13 0 2.247790649e-09 2.876207868e-10 Iteration (num., iy, grad. norm, func.) : 14 0 1.512050677e-09 2.874786667e-10 Iteration (num., iy, grad. norm, func.) : 15 0 3.609275143e-09 2.872620463e-10 Iteration (num., iy, grad. norm, func.) : 16 0 4.433709820e-10 2.867396778e-10 Iteration (num., iy, grad. norm, func.) : 17 0 2.496800518e-10 2.867367691e-10 Iteration (num., iy, grad. norm, func.) : 18 0 1.248267600e-09 2.867322407e-10 Iteration (num., iy, grad. norm, func.) : 19 0 4.542676797e-10 2.866856880e-10 Iteration (num., iy, grad. norm, func.) : 20 0 1.142461233e-09 2.866768805e-10 Iteration (num., iy, grad. norm, func.) : 21 0 3.518825494e-10 2.866050691e-10 Iteration (num., iy, grad. norm, func.) : 22 0 5.728450541e-10 2.865809700e-10 Iteration (num., iy, grad. norm, func.) : 23 0 3.010375451e-10 2.865582760e-10 Iteration (num., iy, grad. norm, func.) : 24 0 4.646038054e-10 2.865505543e-10 Iteration (num., iy, grad. norm, func.) : 25 0 2.741412951e-10 2.865409788e-10 Iteration (num., iy, grad. norm, func.) : 26 0 3.916274212e-10 2.865294244e-10 Iteration (num., iy, grad. norm, func.) : 27 0 2.548934938e-10 2.865167624e-10 Iteration (num., iy, grad. norm, func.) : 28 0 1.866884307e-10 2.865097019e-10 Iteration (num., iy, grad. norm, func.) : 29 0 2.863664727e-10 2.865073043e-10 Iteration (num., iy, grad. norm, func.) : 30 0 1.744332186e-10 2.865050504e-10 Iteration (num., iy, grad. norm, func.) : 31 0 2.167504214e-10 2.865034390e-10 Iteration (num., iy, grad. norm, func.) : 32 0 1.134260875e-10 2.865006839e-10 Iteration (num., iy, grad. norm, func.) : 33 0 1.574862995e-10 2.864975544e-10 Iteration (num., iy, grad. norm, func.) : 34 0 4.824600446e-11 2.864946039e-10 Iteration (num., iy, grad. norm, func.) : 35 0 4.451682046e-11 2.864944625e-10 Iteration (num., iy, grad. norm, func.) : 36 0 5.588069321e-11 2.864942826e-10 Iteration (num., iy, grad. norm, func.) : 37 0 6.154401521e-11 2.864940091e-10 Iteration (num., iy, grad. norm, func.) : 38 0 6.823581622e-11 2.864935946e-10 Iteration (num., iy, grad. norm, func.) : 39 0 4.174059514e-11 2.864931933e-10 Iteration (num., iy, grad. norm, func.) : 40 0 2.893931426e-11 2.864931009e-10 Iteration (num., iy, grad. norm, func.) : 41 0 4.539814138e-11 2.864929669e-10 Iteration (num., iy, grad. norm, func.) : 42 0 2.466568434e-11 2.864928406e-10 Iteration (num., iy, grad. norm, func.) : 43 0 4.529576874e-11 2.864927539e-10 Iteration (num., iy, grad. norm, func.) : 44 0 1.548376023e-11 2.864926735e-10 Iteration (num., iy, grad. norm, func.) : 45 0 2.508731202e-11 2.864926446e-10 Iteration (num., iy, grad. norm, func.) : 46 0 1.420495919e-11 2.864925940e-10 Iteration (num., iy, grad. norm, func.) : 47 0 2.205651344e-11 2.864925596e-10 Iteration (num., iy, grad. norm, func.) : 48 0 9.679171011e-12 2.864925115e-10 Iteration (num., iy, grad. norm, func.) : 49 0 1.423642822e-11 2.864924974e-10 Iteration (num., iy, grad. norm, func.) : 50 0 1.150310283e-11 2.864924858e-10 Iteration (num., iy, grad. norm, func.) : 51 0 9.985115673e-12 2.864924436e-10 Iteration (num., iy, grad. norm, func.) : 52 0 4.325538797e-12 2.864924246e-10 Iteration (num., iy, grad. norm, func.) : 53 0 4.324044881e-12 2.864924246e-10 Iteration (num., iy, grad. norm, func.) : 54 0 4.304543184e-12 2.864924246e-10 Iteration (num., iy, grad. norm, func.) : 55 0 4.641764688e-12 2.864924217e-10 Iteration (num., iy, grad. norm, func.) : 56 0 1.388374801e-12 2.864924185e-10 Iteration (num., iy, grad. norm, func.) : 57 0 2.007280330e-12 2.864924183e-10 Iteration (num., iy, grad. norm, func.) : 58 0 2.152823589e-12 2.864924178e-10 Iteration (num., iy, grad. norm, func.) : 59 0 2.792847949e-12 2.864924174e-10 Iteration (num., iy, grad. norm, func.) : 60 0 1.549756182e-12 2.864924172e-10 Iteration (num., iy, grad. norm, func.) : 61 0 2.660765151e-12 2.864924166e-10 Iteration (num., iy, grad. norm, func.) : 62 0 9.058260844e-13 2.864924158e-10 Solving for output 0 - done. Time (sec): 1.1510787 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 1.422938883e-05 6.489189054e-06 Iteration (num., iy, grad. norm, func.) : 0 1 1.422426134e-05 6.242643394e-06 Iteration (num., iy, grad. norm, func.) : 1 1 1.468644644e-05 8.046322464e-07 Iteration (num., iy, grad. norm, func.) : 2 1 1.980829934e-05 3.853967070e-07 Iteration (num., iy, grad. norm, func.) : 3 1 6.149803188e-06 1.326901223e-07 Iteration (num., iy, grad. norm, func.) : 4 1 5.514652283e-06 1.083596434e-07 Iteration (num., iy, grad. norm, func.) : 5 1 1.681671136e-06 3.942980521e-08 Iteration (num., iy, grad. norm, func.) : 6 1 1.311263507e-06 3.188700938e-08 Iteration (num., iy, grad. norm, func.) : 7 1 1.042778091e-06 3.098470569e-08 Iteration (num., iy, grad. norm, func.) : 8 1 5.949946674e-07 2.935838506e-08 Iteration (num., iy, grad. norm, func.) : 9 1 1.837095130e-07 2.336750770e-08 Iteration (num., iy, grad. norm, func.) : 10 1 1.024120702e-07 1.803239328e-08 Iteration (num., iy, grad. norm, func.) : 11 1 4.560016252e-08 1.512381840e-08 Iteration (num., iy, grad. norm, func.) : 12 1 4.069079267e-08 1.484797776e-08 Iteration (num., iy, grad. norm, func.) : 13 1 4.069079267e-08 1.484797776e-08 Iteration (num., iy, grad. norm, func.) : 14 1 4.069079267e-08 1.484797776e-08 Iteration (num., iy, grad. norm, func.) : 15 1 3.533011076e-08 1.478206340e-08 Iteration (num., iy, grad. norm, func.) : 16 1 2.307870255e-08 1.465456167e-08 Iteration (num., iy, grad. norm, func.) : 17 1 2.093728910e-08 1.458023846e-08 Iteration (num., iy, grad. norm, func.) : 18 1 1.150417427e-08 1.451706578e-08 Iteration (num., iy, grad. norm, func.) : 19 1 7.998452099e-09 1.449203218e-08 Iteration (num., iy, grad. norm, func.) : 20 1 8.061749901e-09 1.449008606e-08 Iteration (num., iy, grad. norm, func.) : 21 1 7.541915802e-09 1.448862605e-08 Iteration (num., iy, grad. norm, func.) : 22 1 1.063867707e-08 1.448377094e-08 Iteration (num., iy, grad. norm, func.) : 23 1 4.857018607e-09 1.447702314e-08 Iteration (num., iy, grad. norm, func.) : 24 1 6.341357316e-09 1.447401552e-08 Iteration (num., iy, grad. norm, func.) : 25 1 3.561692708e-09 1.447090211e-08 Iteration (num., iy, grad. norm, func.) : 26 1 6.731711790e-09 1.446900010e-08 Iteration (num., iy, grad. norm, func.) : 27 1 1.571562669e-09 1.446712148e-08 Iteration (num., iy, grad. norm, func.) : 28 1 2.944549357e-09 1.446709230e-08 Iteration (num., iy, grad. norm, func.) : 29 1 2.230547757e-09 1.446658757e-08 Iteration (num., iy, grad. norm, func.) : 30 1 3.389783471e-09 1.446606337e-08 Iteration (num., iy, grad. norm, func.) : 31 1 1.702346016e-09 1.446509203e-08 Iteration (num., iy, grad. norm, func.) : 32 1 1.801538274e-09 1.446446791e-08 Iteration (num., iy, grad. norm, func.) : 33 1 8.220960597e-10 1.446415726e-08 Iteration (num., iy, grad. norm, func.) : 34 1 7.967134401e-10 1.446407546e-08 Iteration (num., iy, grad. norm, func.) : 35 1 9.007869020e-10 1.446401984e-08 Iteration (num., iy, grad. norm, func.) : 36 1 1.398823471e-09 1.446393572e-08 Iteration (num., iy, grad. norm, func.) : 37 1 6.179297314e-10 1.446384093e-08 Iteration (num., iy, grad. norm, func.) : 38 1 9.792102584e-10 1.446373558e-08 Iteration (num., iy, grad. norm, func.) : 39 1 3.124943095e-10 1.446364015e-08 Iteration (num., iy, grad. norm, func.) : 40 1 3.122610001e-10 1.446363327e-08 Iteration (num., iy, grad. norm, func.) : 41 1 2.906225035e-10 1.446362445e-08 Iteration (num., iy, grad. norm, func.) : 42 1 4.498402993e-10 1.446360941e-08 Iteration (num., iy, grad. norm, func.) : 43 1 3.504886327e-10 1.446358012e-08 Iteration (num., iy, grad. norm, func.) : 44 1 1.051533071e-10 1.446356658e-08 Iteration (num., iy, grad. norm, func.) : 45 1 1.029088597e-10 1.446356657e-08 Iteration (num., iy, grad. norm, func.) : 46 1 9.760858285e-11 1.446356650e-08 Iteration (num., iy, grad. norm, func.) : 47 1 1.653862000e-10 1.446356537e-08 Iteration (num., iy, grad. norm, func.) : 48 1 7.269692555e-11 1.446356368e-08 Iteration (num., iy, grad. norm, func.) : 49 1 1.835035106e-10 1.446356308e-08 Iteration (num., iy, grad. norm, func.) : 50 1 7.017414129e-11 1.446356177e-08 Iteration (num., iy, grad. norm, func.) : 51 1 5.674642093e-11 1.446356064e-08 Iteration (num., iy, grad. norm, func.) : 52 1 6.385672542e-11 1.446356051e-08 Iteration (num., iy, grad. norm, func.) : 53 1 4.403979266e-11 1.446356033e-08 Iteration (num., iy, grad. norm, func.) : 54 1 6.103719588e-11 1.446356003e-08 Iteration (num., iy, grad. norm, func.) : 55 1 2.598341540e-11 1.446355963e-08 Iteration (num., iy, grad. norm, func.) : 56 1 3.749284045e-11 1.446355958e-08 Iteration (num., iy, grad. norm, func.) : 57 1 2.948894440e-11 1.446355958e-08 Iteration (num., iy, grad. norm, func.) : 58 1 4.349011114e-11 1.446355945e-08 Iteration (num., iy, grad. norm, func.) : 59 1 1.150489306e-11 1.446355931e-08 Iteration (num., iy, grad. norm, func.) : 60 1 2.030175252e-11 1.446355930e-08 Iteration (num., iy, grad. norm, func.) : 61 1 1.505670170e-11 1.446355928e-08 Iteration (num., iy, grad. norm, func.) : 62 1 2.500274676e-11 1.446355926e-08 Iteration (num., iy, grad. norm, func.) : 63 1 9.998184321e-12 1.446355922e-08 Iteration (num., iy, grad. norm, func.) : 64 1 1.174683169e-11 1.446355920e-08 Iteration (num., iy, grad. norm, func.) : 65 1 7.818126211e-12 1.446355918e-08 Iteration (num., iy, grad. norm, func.) : 66 1 1.344365437e-11 1.446355918e-08 Iteration (num., iy, grad. norm, func.) : 67 1 6.259601480e-12 1.446355918e-08 Iteration (num., iy, grad. norm, func.) : 68 1 8.863962464e-12 1.446355916e-08 Iteration (num., iy, grad. norm, func.) : 69 1 2.095663893e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 70 1 1.682008437e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 71 1 1.904057340e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 72 1 1.849606450e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 73 1 2.252138274e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 74 1 1.900456942e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 75 1 2.022349276e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 76 1 2.369216629e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 77 1 7.696623094e-13 1.446355915e-08 Solving for output 1 - done. Time (sec): 1.3746848 Solving nonlinear problem (n=1764) - done. Time (sec): 2.5257635 Solving for degrees of freedom - done. Time (sec): 2.5570033 Training - done. Time (sec): 2.5729759 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0021315 Prediction time/pt. (sec) : 0.0000043 ___________________________________________________________________________ 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): 0.0084257 Prediction time/pt. (sec) : 0.0000169 ___________________________________________________________________________ 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): 0.0084097 Prediction time/pt. (sec) : 0.0000168 ___________________________________________________________________________ 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): 0.0024054 Prediction time/pt. (sec) : 0.0000048 ___________________________________________________________________________ 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): 0.0084326 Prediction time/pt. (sec) : 0.0000034 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0024102 Prediction time/pt. (sec) : 0.0000010 .. figure:: rans_crm_wing.png :scale: 60 % :align: center