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): 0.0039933 Computing approximation terms ... Computing approximation terms - done. Time (sec): 0.0000000 Pre-computing matrices - done. Time (sec): 0.0039933 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 6.229432982e-09 1.793038469e-10 Solving for output 0 - done. Time (sec): 0.0055189 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 1.955493282e+00 4.799845498e+00 Iteration (num., iy, grad. norm, func.) : 0 1 2.880003477e-07 4.567660731e-08 Solving for output 1 - done. Time (sec): 0.0049999 Solving initial startup problem (n=400) - done. Time (sec): 0.0105188 Solving nonlinear problem (n=400) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 6.652713506e-09 1.793037943e-10 Iteration (num., iy, grad. norm, func.) : 0 0 5.849717047e-09 1.703954640e-10 Iteration (num., iy, grad. norm, func.) : 1 0 3.028427146e-08 1.034023971e-10 Iteration (num., iy, grad. norm, func.) : 2 0 1.125871064e-08 2.505156303e-11 Iteration (num., iy, grad. norm, func.) : 3 0 3.657654439e-09 1.062711951e-11 Iteration (num., iy, grad. norm, func.) : 4 0 2.384395507e-09 9.420573075e-12 Iteration (num., iy, grad. norm, func.) : 5 0 6.795760320e-10 7.398469916e-12 Iteration (num., iy, grad. norm, func.) : 6 0 1.893580345e-10 6.530836833e-12 Iteration (num., iy, grad. norm, func.) : 7 0 3.914693345e-11 6.262121148e-12 Iteration (num., iy, grad. norm, func.) : 8 0 2.607684883e-11 6.261389686e-12 Iteration (num., iy, grad. norm, func.) : 9 0 1.596473148e-11 6.260468564e-12 Iteration (num., iy, grad. norm, func.) : 10 0 8.775713015e-12 6.260124667e-12 Iteration (num., iy, grad. norm, func.) : 11 0 3.358656828e-12 6.256629410e-12 Iteration (num., iy, grad. norm, func.) : 12 0 5.210392100e-13 6.255688670e-12 Solving for output 0 - done. Time (sec): 0.0631311 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 9.729377248e-08 4.567642993e-08 Iteration (num., iy, grad. norm, func.) : 0 1 9.338363991e-08 4.538219456e-08 Iteration (num., iy, grad. norm, func.) : 1 1 2.891391770e-06 3.242337200e-08 Iteration (num., iy, grad. norm, func.) : 2 1 8.605107278e-07 4.652778039e-09 Iteration (num., iy, grad. norm, func.) : 3 1 5.067375622e-07 2.745465684e-09 Iteration (num., iy, grad. norm, func.) : 4 1 4.513840269e-07 2.455226693e-09 Iteration (num., iy, grad. norm, func.) : 5 1 1.339167789e-07 6.881109113e-10 Iteration (num., iy, grad. norm, func.) : 6 1 6.591489027e-08 5.500475184e-10 Iteration (num., iy, grad. norm, func.) : 7 1 3.839790426e-08 4.862409238e-10 Iteration (num., iy, grad. norm, func.) : 8 1 1.327787768e-08 4.053646260e-10 Iteration (num., iy, grad. norm, func.) : 9 1 4.178809432e-09 3.129533457e-10 Iteration (num., iy, grad. norm, func.) : 10 1 1.162280444e-09 2.724104537e-10 Iteration (num., iy, grad. norm, func.) : 11 1 9.071092861e-10 2.722854123e-10 Iteration (num., iy, grad. norm, func.) : 12 1 5.267790369e-10 2.722199641e-10 Iteration (num., iy, grad. norm, func.) : 13 1 3.128933500e-10 2.721466104e-10 Iteration (num., iy, grad. norm, func.) : 14 1 1.223449033e-10 2.716612344e-10 Iteration (num., iy, grad. norm, func.) : 15 1 3.662769998e-11 2.714261096e-10 Iteration (num., iy, grad. norm, func.) : 16 1 1.874681541e-11 2.713872039e-10 Iteration (num., iy, grad. norm, func.) : 17 1 4.264650696e-11 2.713834397e-10 Iteration (num., iy, grad. norm, func.) : 18 1 9.492032166e-12 2.713607454e-10 Iteration (num., iy, grad. norm, func.) : 19 1 1.706167514e-11 2.713578816e-10 Iteration (num., iy, grad. norm, func.) : 20 1 7.138581938e-12 2.713509077e-10 Iteration (num., iy, grad. norm, func.) : 21 1 8.869098799e-12 2.713470556e-10 Iteration (num., iy, grad. norm, func.) : 22 1 7.413215243e-12 2.713459004e-10 Iteration (num., iy, grad. norm, func.) : 23 1 2.698341151e-12 2.713454840e-10 Iteration (num., iy, grad. norm, func.) : 24 1 2.260359199e-12 2.713454159e-10 Iteration (num., iy, grad. norm, func.) : 25 1 2.987484747e-12 2.713452000e-10 Iteration (num., iy, grad. norm, func.) : 26 1 9.299976611e-13 2.713450184e-10 Solving for output 1 - done. Time (sec): 0.1322930 Solving nonlinear problem (n=400) - done. Time (sec): 0.1954241 Solving for degrees of freedom - done. Time (sec): 0.2059429 Training - done. Time (sec): 0.2109408 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0010037 Prediction time/pt. (sec) : 0.0000020 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0010016 Prediction time/pt. (sec) : 0.0000020 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 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): 0.0010087 Prediction time/pt. (sec) : 0.0000020 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0009997 Prediction time/pt. (sec) : 0.0000004 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0009985 Prediction time/pt. (sec) : 0.0000004 .. 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): 0.0025086 Initializing Hessian ... Initializing Hessian - done. Time (sec): 0.0000000 Computing energy terms ... Computing energy terms - done. Time (sec): 0.0080247 Computing approximation terms ... Computing approximation terms - done. Time (sec): 0.0010026 Pre-computing matrices - done. Time (sec): 0.0115359 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 2.575575620e-06 2.207577304e-08 Solving for output 0 - done. Time (sec): 0.0190427 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 5.806995269e-05 6.501953946e-06 Solving for output 1 - done. Time (sec): 0.0120237 Solving initial startup problem (n=1764) - done. Time (sec): 0.0310664 Solving nonlinear problem (n=1764) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 8.737858409e-07 2.207058354e-08 Iteration (num., iy, grad. norm, func.) : 0 0 9.583937937e-07 1.752349703e-08 Iteration (num., iy, grad. norm, func.) : 1 0 3.546737468e-07 3.273320789e-09 Iteration (num., iy, grad. norm, func.) : 2 0 1.183236974e-07 1.053100246e-09 Iteration (num., iy, grad. norm, func.) : 3 0 6.339993507e-08 5.347470320e-10 Iteration (num., iy, grad. norm, func.) : 4 0 3.374476763e-08 4.103939213e-10 Iteration (num., iy, grad. norm, func.) : 5 0 2.244269022e-08 3.752837377e-10 Iteration (num., iy, grad. norm, func.) : 6 0 1.965616160e-08 3.750955278e-10 Iteration (num., iy, grad. norm, func.) : 7 0 1.527792816e-08 3.661190229e-10 Iteration (num., iy, grad. norm, func.) : 8 0 1.694107204e-08 3.641575713e-10 Iteration (num., iy, grad. norm, func.) : 9 0 1.400350042e-08 3.400159769e-10 Iteration (num., iy, grad. norm, func.) : 10 0 8.618937187e-09 3.088359753e-10 Iteration (num., iy, grad. norm, func.) : 11 0 2.752861021e-09 2.905779707e-10 Iteration (num., iy, grad. norm, func.) : 12 0 2.459715986e-09 2.894122635e-10 Iteration (num., iy, grad. norm, func.) : 13 0 2.459715986e-09 2.894122635e-10 Iteration (num., iy, grad. norm, func.) : 14 0 2.459715986e-09 2.894122635e-10 Iteration (num., iy, grad. norm, func.) : 15 0 3.790841098e-09 2.884227179e-10 Iteration (num., iy, grad. norm, func.) : 16 0 7.660403189e-10 2.872925091e-10 Iteration (num., iy, grad. norm, func.) : 17 0 1.462954522e-09 2.870607852e-10 Iteration (num., iy, grad. norm, func.) : 18 0 9.736562709e-10 2.869808466e-10 Iteration (num., iy, grad. norm, func.) : 19 0 9.254952451e-10 2.869603286e-10 Iteration (num., iy, grad. norm, func.) : 20 0 8.664460782e-10 2.869027351e-10 Iteration (num., iy, grad. norm, func.) : 21 0 1.082182834e-09 2.867531901e-10 Iteration (num., iy, grad. norm, func.) : 22 0 7.203857332e-10 2.866272767e-10 Iteration (num., iy, grad. norm, func.) : 23 0 3.764133529e-10 2.865649123e-10 Iteration (num., iy, grad. norm, func.) : 24 0 3.282663853e-10 2.865624972e-10 Iteration (num., iy, grad. norm, func.) : 25 0 4.358374191e-10 2.865622292e-10 Iteration (num., iy, grad. norm, func.) : 26 0 4.203829589e-10 2.865539364e-10 Iteration (num., iy, grad. norm, func.) : 27 0 4.236541716e-10 2.865430468e-10 Iteration (num., iy, grad. norm, func.) : 28 0 2.549813945e-10 2.865278740e-10 Iteration (num., iy, grad. norm, func.) : 29 0 3.175902813e-10 2.865200228e-10 Iteration (num., iy, grad. norm, func.) : 30 0 1.818129781e-10 2.865131410e-10 Iteration (num., iy, grad. norm, func.) : 31 0 2.432582109e-10 2.865037093e-10 Iteration (num., iy, grad. norm, func.) : 32 0 8.721803489e-11 2.864965834e-10 Iteration (num., iy, grad. norm, func.) : 33 0 7.322984911e-11 2.864965493e-10 Iteration (num., iy, grad. norm, func.) : 34 0 8.732258237e-11 2.864961378e-10 Iteration (num., iy, grad. norm, func.) : 35 0 8.804680263e-11 2.864954282e-10 Iteration (num., iy, grad. norm, func.) : 36 0 8.914333813e-11 2.864951327e-10 Iteration (num., iy, grad. norm, func.) : 37 0 8.038605550e-11 2.864948918e-10 Iteration (num., iy, grad. norm, func.) : 38 0 1.211396047e-10 2.864945162e-10 Iteration (num., iy, grad. norm, func.) : 39 0 4.409752418e-11 2.864938221e-10 Iteration (num., iy, grad. norm, func.) : 40 0 5.330912907e-11 2.864934684e-10 Iteration (num., iy, grad. norm, func.) : 41 0 3.762907472e-11 2.864932279e-10 Iteration (num., iy, grad. norm, func.) : 42 0 5.711461398e-11 2.864930324e-10 Iteration (num., iy, grad. norm, func.) : 43 0 2.453269461e-11 2.864928261e-10 Iteration (num., iy, grad. norm, func.) : 44 0 3.736869281e-11 2.864928135e-10 Iteration (num., iy, grad. norm, func.) : 45 0 2.321967355e-11 2.864927721e-10 Iteration (num., iy, grad. norm, func.) : 46 0 3.910640430e-11 2.864927177e-10 Iteration (num., iy, grad. norm, func.) : 47 0 1.788968560e-11 2.864926239e-10 Iteration (num., iy, grad. norm, func.) : 48 0 2.002815893e-11 2.864925521e-10 Iteration (num., iy, grad. norm, func.) : 49 0 1.180675297e-11 2.864925052e-10 Iteration (num., iy, grad. norm, func.) : 50 0 1.941442472e-11 2.864925043e-10 Iteration (num., iy, grad. norm, func.) : 51 0 1.146499516e-11 2.864925028e-10 Iteration (num., iy, grad. norm, func.) : 52 0 1.650029728e-11 2.864924929e-10 Iteration (num., iy, grad. norm, func.) : 53 0 1.070168524e-11 2.864924715e-10 Iteration (num., iy, grad. norm, func.) : 54 0 1.177117044e-11 2.864924480e-10 Iteration (num., iy, grad. norm, func.) : 55 0 4.190805945e-12 2.864924309e-10 Iteration (num., iy, grad. norm, func.) : 56 0 3.466624617e-12 2.864924292e-10 Iteration (num., iy, grad. norm, func.) : 57 0 4.187491808e-12 2.864924274e-10 Iteration (num., iy, grad. norm, func.) : 58 0 4.908088917e-12 2.864924250e-10 Iteration (num., iy, grad. norm, func.) : 59 0 6.094730716e-12 2.864924232e-10 Iteration (num., iy, grad. norm, func.) : 60 0 3.555167666e-12 2.864924217e-10 Iteration (num., iy, grad. norm, func.) : 61 0 4.667159382e-12 2.864924216e-10 Iteration (num., iy, grad. norm, func.) : 62 0 2.758935157e-12 2.864924203e-10 Iteration (num., iy, grad. norm, func.) : 63 0 4.025897384e-12 2.864924191e-10 Iteration (num., iy, grad. norm, func.) : 64 0 1.886632298e-12 2.864924177e-10 Iteration (num., iy, grad. norm, func.) : 65 0 2.858509935e-12 2.864924172e-10 Iteration (num., iy, grad. norm, func.) : 66 0 1.505715565e-12 2.864924169e-10 Iteration (num., iy, grad. norm, func.) : 67 0 2.664590083e-12 2.864924165e-10 Iteration (num., iy, grad. norm, func.) : 68 0 8.712580952e-13 2.864924159e-10 Solving for output 0 - done. Time (sec): 0.9173832 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 1.434042246e-05 6.499348875e-06 Iteration (num., iy, grad. norm, func.) : 0 1 1.434144082e-05 6.252435785e-06 Iteration (num., iy, grad. norm, func.) : 1 1 1.476589235e-05 8.057263488e-07 Iteration (num., iy, grad. norm, func.) : 2 1 1.795902459e-05 3.606941390e-07 Iteration (num., iy, grad. norm, func.) : 3 1 5.530739183e-06 1.259947411e-07 Iteration (num., iy, grad. norm, func.) : 4 1 4.450520917e-06 9.727596048e-08 Iteration (num., iy, grad. norm, func.) : 5 1 1.368980438e-06 3.501563197e-08 Iteration (num., iy, grad. norm, func.) : 6 1 1.020853131e-06 3.008202531e-08 Iteration (num., iy, grad. norm, func.) : 7 1 7.823302514e-07 2.972410388e-08 Iteration (num., iy, grad. norm, func.) : 8 1 5.069636419e-07 2.921779973e-08 Iteration (num., iy, grad. norm, func.) : 9 1 1.871226190e-07 2.355740343e-08 Iteration (num., iy, grad. norm, func.) : 10 1 8.846817742e-08 1.806067121e-08 Iteration (num., iy, grad. norm, func.) : 11 1 4.191727220e-08 1.505842598e-08 Iteration (num., iy, grad. norm, func.) : 12 1 3.298523058e-08 1.477470833e-08 Iteration (num., iy, grad. norm, func.) : 13 1 3.298523058e-08 1.477470833e-08 Iteration (num., iy, grad. norm, func.) : 14 1 3.298523058e-08 1.477470833e-08 Iteration (num., iy, grad. norm, func.) : 15 1 3.527910738e-08 1.467890057e-08 Iteration (num., iy, grad. norm, func.) : 16 1 1.052194537e-08 1.453974951e-08 Iteration (num., iy, grad. norm, func.) : 17 1 1.462841687e-08 1.451934915e-08 Iteration (num., iy, grad. norm, func.) : 18 1 1.106621818e-08 1.450927248e-08 Iteration (num., iy, grad. norm, func.) : 19 1 1.458451265e-08 1.449957379e-08 Iteration (num., iy, grad. norm, func.) : 20 1 7.422220754e-09 1.449817301e-08 Iteration (num., iy, grad. norm, func.) : 21 1 1.266731481e-08 1.449629744e-08 Iteration (num., iy, grad. norm, func.) : 22 1 4.873470821e-09 1.448291296e-08 Iteration (num., iy, grad. norm, func.) : 23 1 5.830688146e-09 1.447443516e-08 Iteration (num., iy, grad. norm, func.) : 24 1 2.702817368e-09 1.446930180e-08 Iteration (num., iy, grad. norm, func.) : 25 1 3.482517704e-09 1.446853284e-08 Iteration (num., iy, grad. norm, func.) : 26 1 2.425197299e-09 1.446779033e-08 Iteration (num., iy, grad. norm, func.) : 27 1 4.384364765e-09 1.446656530e-08 Iteration (num., iy, grad. norm, func.) : 28 1 1.666807655e-09 1.446520269e-08 Iteration (num., iy, grad. norm, func.) : 29 1 1.146121195e-09 1.446505644e-08 Iteration (num., iy, grad. norm, func.) : 30 1 1.957016230e-09 1.446485007e-08 Iteration (num., iy, grad. norm, func.) : 31 1 1.400902411e-09 1.446446069e-08 Iteration (num., iy, grad. norm, func.) : 32 1 2.024876704e-09 1.446420260e-08 Iteration (num., iy, grad. norm, func.) : 33 1 7.392307757e-10 1.446402715e-08 Iteration (num., iy, grad. norm, func.) : 34 1 6.113871401e-10 1.446396613e-08 Iteration (num., iy, grad. norm, func.) : 35 1 8.897950108e-10 1.446387799e-08 Iteration (num., iy, grad. norm, func.) : 36 1 8.435572508e-10 1.446376880e-08 Iteration (num., iy, grad. norm, func.) : 37 1 6.781034056e-10 1.446369127e-08 Iteration (num., iy, grad. norm, func.) : 38 1 4.359785203e-10 1.446365605e-08 Iteration (num., iy, grad. norm, func.) : 39 1 3.604168165e-10 1.446364595e-08 Iteration (num., iy, grad. norm, func.) : 40 1 4.714920931e-10 1.446363720e-08 Iteration (num., iy, grad. norm, func.) : 41 1 4.111309595e-10 1.446361859e-08 Iteration (num., iy, grad. norm, func.) : 42 1 2.935425518e-10 1.446360051e-08 Iteration (num., iy, grad. norm, func.) : 43 1 4.007380257e-10 1.446358478e-08 Iteration (num., iy, grad. norm, func.) : 44 1 1.363139815e-10 1.446357223e-08 Iteration (num., iy, grad. norm, func.) : 45 1 9.870853160e-11 1.446357135e-08 Iteration (num., iy, grad. norm, func.) : 46 1 1.398078199e-10 1.446357010e-08 Iteration (num., iy, grad. norm, func.) : 47 1 1.301007213e-10 1.446356763e-08 Iteration (num., iy, grad. norm, func.) : 48 1 1.641898408e-10 1.446356566e-08 Iteration (num., iy, grad. norm, func.) : 49 1 1.162937331e-10 1.446356439e-08 Iteration (num., iy, grad. norm, func.) : 50 1 1.192123410e-10 1.446356022e-08 Iteration (num., iy, grad. norm, func.) : 51 1 6.556032628e-11 1.446355959e-08 Iteration (num., iy, grad. norm, func.) : 52 1 6.137545986e-11 1.446355959e-08 Iteration (num., iy, grad. norm, func.) : 53 1 4.468566796e-11 1.446355957e-08 Iteration (num., iy, grad. norm, func.) : 54 1 2.996918496e-11 1.446355952e-08 Iteration (num., iy, grad. norm, func.) : 55 1 1.980844385e-11 1.446355940e-08 Iteration (num., iy, grad. norm, func.) : 56 1 1.794802120e-11 1.446355928e-08 Iteration (num., iy, grad. norm, func.) : 57 1 9.147072506e-12 1.446355920e-08 Iteration (num., iy, grad. norm, func.) : 58 1 1.229585725e-11 1.446355919e-08 Iteration (num., iy, grad. norm, func.) : 59 1 9.365521643e-12 1.446355919e-08 Iteration (num., iy, grad. norm, func.) : 60 1 1.406243787e-11 1.446355918e-08 Iteration (num., iy, grad. norm, func.) : 61 1 5.522603866e-12 1.446355916e-08 Iteration (num., iy, grad. norm, func.) : 62 1 6.529863254e-12 1.446355916e-08 Iteration (num., iy, grad. norm, func.) : 63 1 4.754418096e-12 1.446355916e-08 Iteration (num., iy, grad. norm, func.) : 64 1 6.124393750e-12 1.446355916e-08 Iteration (num., iy, grad. norm, func.) : 65 1 6.089857348e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 66 1 1.636095665e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 67 1 1.518616253e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 68 1 2.823435682e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 69 1 1.718325361e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 70 1 1.805099003e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 71 1 1.353303475e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 72 1 1.740106987e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 73 1 4.164410037e-13 1.446355915e-08 Solving for output 1 - done. Time (sec): 0.9746635 Solving nonlinear problem (n=1764) - done. Time (sec): 1.8920467 Solving for degrees of freedom - done. Time (sec): 1.9231131 Training - done. Time (sec): 1.9346490 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0009995 Prediction time/pt. (sec) : 0.0000020 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0009997 Prediction time/pt. (sec) : 0.0000020 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0009987 Prediction time/pt. (sec) : 0.0000020 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0000000 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): 0.0019970 Prediction time/pt. (sec) : 0.0000008 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0020299 Prediction time/pt. (sec) : 0.0000008 .. figure:: rans_crm_wing.png :scale: 60 % :align: center