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): 0.0156271 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 .. 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): 0.0166361 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 .. figure:: rans_crm_wing.png :scale: 60 % :align: center