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 numpy as np import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt rad2deg = 180.0 / np.pi num = 500 num_a = 50 num_M = 50 x = np.zeros((num, 2)) colors = ["b", "g", "r", "c", "m", "k", "y"] nrow = 3 ncol = 2 plt.close() fig, axs = plt.subplots(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.surrogate_models import RMTB from smt.examples.rans_crm_wing.rans_crm_wing import ( get_rans_crm_wing, plot_rans_crm_wing, ) xt, yt, xlimits = get_rans_crm_wing() interp = RMTB( num_ctrl_pts=20, xlimits=xlimits, nonlinear_maxiter=100, energy_weight=1e-12 ) interp.set_training_values(xt, yt) interp.train() plot_rans_crm_wing(xt, yt, xlimits, interp) :: ___________________________________________________________________________ RMTB ___________________________________________________________________________ Problem size # training points. : 35 ___________________________________________________________________________ Training Training ... Pre-computing matrices ... Computing dof2coeff ... Computing dof2coeff - done. Time (sec): 0.0000010 Initializing Hessian ... Initializing Hessian - done. Time (sec): 0.0001640 Computing energy terms ... Computing energy terms - done. Time (sec): 0.0015941 Computing approximation terms ... Computing approximation terms - done. Time (sec): 0.0001333 Pre-computing matrices - done. Time (sec): 0.0019090 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 5.411685665e-09 1.793038265e-10 Solving for output 0 - done. Time (sec): 0.0034111 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 1.152882116e-06 4.567837893e-08 Solving for output 1 - done. Time (sec): 0.0032072 Solving initial startup problem (n=400) - done. Time (sec): 0.0066359 Solving nonlinear problem (n=400) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 6.652710113e-09 1.793037477e-10 Iteration (num., iy, grad. norm, func.) : 0 0 5.849768196e-09 1.703944261e-10 Iteration (num., iy, grad. norm, func.) : 1 0 3.016454001e-08 1.031027582e-10 Iteration (num., iy, grad. norm, func.) : 2 0 1.124710921e-08 2.503452196e-11 Iteration (num., iy, grad. norm, func.) : 3 0 3.558936270e-09 1.052011492e-11 Iteration (num., iy, grad. norm, func.) : 4 0 2.498908321e-09 9.513167500e-12 Iteration (num., iy, grad. norm, func.) : 5 0 7.337648484e-10 7.423057662e-12 Iteration (num., iy, grad. norm, func.) : 6 0 2.035878919e-10 6.536207916e-12 Iteration (num., iy, grad. norm, func.) : 7 0 4.339677721e-11 6.262717357e-12 Iteration (num., iy, grad. norm, func.) : 8 0 2.610301695e-11 6.261662965e-12 Iteration (num., iy, grad. norm, func.) : 9 0 1.543232359e-11 6.260740582e-12 Iteration (num., iy, grad. norm, func.) : 10 0 1.329544861e-11 6.260413869e-12 Iteration (num., iy, grad. norm, func.) : 11 0 3.816926844e-12 6.256688224e-12 Iteration (num., iy, grad. norm, func.) : 12 0 5.374725819e-13 6.255690501e-12 Solving for output 0 - done. Time (sec): 0.0417490 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 9.727807753e-08 4.567646553e-08 Iteration (num., iy, grad. norm, func.) : 0 1 9.336806869e-08 4.538213478e-08 Iteration (num., iy, grad. norm, func.) : 1 1 2.895998969e-06 3.242270961e-08 Iteration (num., iy, grad. norm, func.) : 2 1 8.585632456e-07 4.646623688e-09 Iteration (num., iy, grad. norm, func.) : 3 1 2.739253784e-07 2.027009051e-09 Iteration (num., iy, grad. norm, func.) : 4 1 2.525438576e-07 1.844466417e-09 Iteration (num., iy, grad. norm, func.) : 5 1 7.493959631e-08 5.722361822e-10 Iteration (num., iy, grad. norm, func.) : 6 1 8.096618492e-08 5.722012169e-10 Iteration (num., iy, grad. norm, func.) : 7 1 2.391970442e-08 4.570786542e-10 Iteration (num., iy, grad. norm, func.) : 8 1 4.025741805e-08 4.502724092e-10 Iteration (num., iy, grad. norm, func.) : 9 1 1.205755219e-08 3.321741389e-10 Iteration (num., iy, grad. norm, func.) : 10 1 5.915127060e-09 2.852783181e-10 Iteration (num., iy, grad. norm, func.) : 11 1 1.748395965e-09 2.745189928e-10 Iteration (num., iy, grad. norm, func.) : 12 1 1.020332111e-09 2.737098068e-10 Iteration (num., iy, grad. norm, func.) : 13 1 2.958281615e-10 2.718558829e-10 Iteration (num., iy, grad. norm, func.) : 14 1 1.244706534e-10 2.716147626e-10 Iteration (num., iy, grad. norm, func.) : 15 1 9.612939394e-11 2.715864299e-10 Iteration (num., iy, grad. norm, func.) : 16 1 4.817932290e-11 2.715105634e-10 Iteration (num., iy, grad. norm, func.) : 17 1 5.807039247e-11 2.713914627e-10 Iteration (num., iy, grad. norm, func.) : 18 1 1.172706159e-11 2.713663434e-10 Iteration (num., iy, grad. norm, func.) : 19 1 1.038579664e-11 2.713659144e-10 Iteration (num., iy, grad. norm, func.) : 20 1 1.441052598e-11 2.713605352e-10 Iteration (num., iy, grad. norm, func.) : 21 1 1.183446666e-11 2.713500816e-10 Iteration (num., iy, grad. norm, func.) : 22 1 7.124663001e-12 2.713459092e-10 Iteration (num., iy, grad. norm, func.) : 23 1 4.711945231e-12 2.713455589e-10 Iteration (num., iy, grad. norm, func.) : 24 1 6.028427248e-12 2.713455455e-10 Iteration (num., iy, grad. norm, func.) : 25 1 2.445826614e-12 2.713453556e-10 Iteration (num., iy, grad. norm, func.) : 26 1 2.370128840e-12 2.713452445e-10 Iteration (num., iy, grad. norm, func.) : 27 1 1.338409467e-12 2.713451003e-10 Iteration (num., iy, grad. norm, func.) : 28 1 1.406364883e-12 2.713450026e-10 Iteration (num., iy, grad. norm, func.) : 29 1 5.971131896e-13 2.713449643e-10 Solving for output 1 - done. Time (sec): 0.0959361 Solving nonlinear problem (n=400) - done. Time (sec): 0.1377010 Solving for degrees of freedom - done. Time (sec): 0.1443558 Training - done. Time (sec): 0.1464341 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001869 Prediction time/pt. (sec) : 0.0000004 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001459 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001600 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001431 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001538 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001421 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001521 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001428 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001850 Prediction time/pt. (sec) : 0.0000004 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001442 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001619 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001411 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001600 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0001409 Prediction time/pt. (sec) : 0.0000003 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0004799 Prediction time/pt. (sec) : 0.0000002 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0011289 Prediction time/pt. (sec) : 0.0000005 .. figure:: rans_crm_wing.png :scale: 60 % :align: center RMTC ---- .. code-block:: python from smt.surrogate_models import RMTC from smt.examples.rans_crm_wing.rans_crm_wing import ( get_rans_crm_wing, plot_rans_crm_wing, ) xt, yt, xlimits = get_rans_crm_wing() interp = RMTC( num_elements=20, xlimits=xlimits, nonlinear_maxiter=100, energy_weight=1e-10 ) interp.set_training_values(xt, yt) interp.train() plot_rans_crm_wing(xt, yt, xlimits, interp) :: ___________________________________________________________________________ RMTC ___________________________________________________________________________ Problem size # training points. : 35 ___________________________________________________________________________ Training Training ... Pre-computing matrices ... Computing dof2coeff ... Computing dof2coeff - done. Time (sec): 0.0019720 Initializing Hessian ... Initializing Hessian - done. Time (sec): 0.0001159 Computing energy terms ... Computing energy terms - done. Time (sec): 0.0067520 Computing approximation terms ... Computing approximation terms - done. Time (sec): 0.0003111 Pre-computing matrices - done. Time (sec): 0.0091741 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 1.259282070e-05 2.109789960e-08 Solving for output 0 - done. Time (sec): 0.0191290 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 1.761847844e-04 6.478283527e-06 Solving for output 1 - done. Time (sec): 0.0228369 Solving initial startup problem (n=1764) - done. Time (sec): 0.0419991 Solving nonlinear problem (n=1764) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 8.276687703e-07 2.096801653e-08 Iteration (num., iy, grad. norm, func.) : 0 0 8.884396364e-07 1.662418478e-08 Iteration (num., iy, grad. norm, func.) : 1 0 3.441659572e-07 3.191045347e-09 Iteration (num., iy, grad. norm, func.) : 2 0 1.140278790e-07 1.028877472e-09 Iteration (num., iy, grad. norm, func.) : 3 0 6.030827518e-08 5.275940591e-10 Iteration (num., iy, grad. norm, func.) : 4 0 3.583981959e-08 4.129899689e-10 Iteration (num., iy, grad. norm, func.) : 5 0 2.344765464e-08 3.785491285e-10 Iteration (num., iy, grad. norm, func.) : 6 0 2.061895441e-08 3.748079064e-10 Iteration (num., iy, grad. norm, func.) : 7 0 2.042895792e-08 3.737771607e-10 Iteration (num., iy, grad. norm, func.) : 8 0 1.357907469e-08 3.613254590e-10 Iteration (num., iy, grad. norm, func.) : 9 0 1.767294044e-08 3.411338356e-10 Iteration (num., iy, grad. norm, func.) : 10 0 5.748755700e-09 3.049820275e-10 Iteration (num., iy, grad. norm, func.) : 11 0 2.156169312e-09 2.891117507e-10 Iteration (num., iy, grad. norm, func.) : 12 0 1.772786849e-09 2.876566274e-10 Iteration (num., iy, grad. norm, func.) : 13 0 2.160274001e-09 2.876190655e-10 Iteration (num., iy, grad. norm, func.) : 14 0 1.490064700e-09 2.874904937e-10 Iteration (num., iy, grad. norm, func.) : 15 0 3.400329977e-09 2.872663534e-10 Iteration (num., iy, grad. norm, func.) : 16 0 4.316611682e-10 2.867323229e-10 Iteration (num., iy, grad. norm, func.) : 17 0 2.489681985e-10 2.867302148e-10 Iteration (num., iy, grad. norm, func.) : 18 0 6.291419172e-10 2.866941738e-10 Iteration (num., iy, grad. norm, func.) : 19 0 4.837469835e-10 2.866369249e-10 Iteration (num., iy, grad. norm, func.) : 20 0 9.283861527e-10 2.865862017e-10 Iteration (num., iy, grad. norm, func.) : 21 0 2.382079194e-10 2.865391052e-10 Iteration (num., iy, grad. norm, func.) : 22 0 1.894975509e-10 2.865387665e-10 Iteration (num., iy, grad. norm, func.) : 23 0 2.405852922e-10 2.865336296e-10 Iteration (num., iy, grad. norm, func.) : 24 0 3.070573255e-10 2.865218301e-10 Iteration (num., iy, grad. norm, func.) : 25 0 2.613049980e-10 2.865083846e-10 Iteration (num., iy, grad. norm, func.) : 26 0 1.308482667e-10 2.865009386e-10 Iteration (num., iy, grad. norm, func.) : 27 0 1.220979706e-10 2.865007984e-10 Iteration (num., iy, grad. norm, func.) : 28 0 1.122777786e-10 2.865001450e-10 Iteration (num., iy, grad. norm, func.) : 29 0 1.726789770e-10 2.864981347e-10 Iteration (num., iy, grad. norm, func.) : 30 0 1.123446546e-10 2.864961197e-10 Iteration (num., iy, grad. norm, func.) : 31 0 5.980430076e-11 2.864952870e-10 Iteration (num., iy, grad. norm, func.) : 32 0 8.090859615e-11 2.864949463e-10 Iteration (num., iy, grad. norm, func.) : 33 0 5.936596229e-11 2.864944374e-10 Iteration (num., iy, grad. norm, func.) : 34 0 7.832680381e-11 2.864939276e-10 Iteration (num., iy, grad. norm, func.) : 35 0 5.663067016e-11 2.864936254e-10 Iteration (num., iy, grad. norm, func.) : 36 0 6.548138612e-11 2.864934878e-10 Iteration (num., iy, grad. norm, func.) : 37 0 4.248302932e-11 2.864932808e-10 Iteration (num., iy, grad. norm, func.) : 38 0 5.706961646e-11 2.864928701e-10 Iteration (num., iy, grad. norm, func.) : 39 0 1.205490677e-11 2.864925499e-10 Iteration (num., iy, grad. norm, func.) : 40 0 9.779697544e-12 2.864925497e-10 Iteration (num., iy, grad. norm, func.) : 41 0 1.505135051e-11 2.864925379e-10 Iteration (num., iy, grad. norm, func.) : 42 0 1.750117773e-11 2.864925219e-10 Iteration (num., iy, grad. norm, func.) : 43 0 2.276253977e-11 2.864925175e-10 Iteration (num., iy, grad. norm, func.) : 44 0 1.242486043e-11 2.864925038e-10 Iteration (num., iy, grad. norm, func.) : 45 0 1.277909442e-11 2.864924940e-10 Iteration (num., iy, grad. norm, func.) : 46 0 9.270563472e-12 2.864924721e-10 Iteration (num., iy, grad. norm, func.) : 47 0 1.423173424e-11 2.864924524e-10 Iteration (num., iy, grad. norm, func.) : 48 0 6.618598844e-12 2.864924381e-10 Iteration (num., iy, grad. norm, func.) : 49 0 8.612779828e-12 2.864924362e-10 Iteration (num., iy, grad. norm, func.) : 50 0 8.016201180e-12 2.864924360e-10 Iteration (num., iy, grad. norm, func.) : 51 0 9.905043751e-12 2.864924334e-10 Iteration (num., iy, grad. norm, func.) : 52 0 5.314899333e-12 2.864924283e-10 Iteration (num., iy, grad. norm, func.) : 53 0 7.643978239e-12 2.864924247e-10 Iteration (num., iy, grad. norm, func.) : 54 0 3.062729209e-12 2.864924217e-10 Iteration (num., iy, grad. norm, func.) : 55 0 5.222165609e-12 2.864924204e-10 Iteration (num., iy, grad. norm, func.) : 56 0 2.729961166e-12 2.864924193e-10 Iteration (num., iy, grad. norm, func.) : 57 0 4.296941506e-12 2.864924190e-10 Iteration (num., iy, grad. norm, func.) : 58 0 2.253107949e-12 2.864924181e-10 Iteration (num., iy, grad. norm, func.) : 59 0 2.888295706e-12 2.864924176e-10 Iteration (num., iy, grad. norm, func.) : 60 0 1.474836055e-12 2.864924169e-10 Iteration (num., iy, grad. norm, func.) : 61 0 2.005862349e-12 2.864924165e-10 Iteration (num., iy, grad. norm, func.) : 62 0 1.072143325e-12 2.864924162e-10 Iteration (num., iy, grad. norm, func.) : 63 0 1.293356797e-12 2.864924161e-10 Iteration (num., iy, grad. norm, func.) : 64 0 8.918332384e-13 2.864924160e-10 Solving for output 0 - done. Time (sec): 0.7125902 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 1.384935414e-05 6.453687706e-06 Iteration (num., iy, grad. norm, func.) : 0 1 1.388050335e-05 6.210060496e-06 Iteration (num., iy, grad. norm, func.) : 1 1 1.452494640e-05 8.014959585e-07 Iteration (num., iy, grad. norm, func.) : 2 1 1.903208528e-05 3.734712156e-07 Iteration (num., iy, grad. norm, func.) : 3 1 5.737186337e-06 1.281344686e-07 Iteration (num., iy, grad. norm, func.) : 4 1 4.457389321e-06 9.689222211e-08 Iteration (num., iy, grad. norm, func.) : 5 1 1.372969382e-06 3.543782462e-08 Iteration (num., iy, grad. norm, func.) : 6 1 7.024568652e-07 2.906732291e-08 Iteration (num., iy, grad. norm, func.) : 7 1 5.433786024e-07 2.860360694e-08 Iteration (num., iy, grad. norm, func.) : 8 1 4.358777169e-07 2.801530768e-08 Iteration (num., iy, grad. norm, func.) : 9 1 1.955146494e-07 2.256768273e-08 Iteration (num., iy, grad. norm, func.) : 10 1 8.458938107e-08 1.740128028e-08 Iteration (num., iy, grad. norm, func.) : 11 1 3.373930461e-08 1.493369323e-08 Iteration (num., iy, grad. norm, func.) : 12 1 3.979022494e-08 1.482361019e-08 Iteration (num., iy, grad. norm, func.) : 13 1 3.979022494e-08 1.482361019e-08 Iteration (num., iy, grad. norm, func.) : 14 1 3.979022493e-08 1.482361019e-08 Iteration (num., iy, grad. norm, func.) : 15 1 3.641259638e-08 1.472420600e-08 Iteration (num., iy, grad. norm, func.) : 16 1 1.311420547e-08 1.457626468e-08 Iteration (num., iy, grad. norm, func.) : 17 1 1.398441040e-08 1.453051901e-08 Iteration (num., iy, grad. norm, func.) : 18 1 9.613776414e-09 1.450298372e-08 Iteration (num., iy, grad. norm, func.) : 19 1 1.370861978e-08 1.449297649e-08 Iteration (num., iy, grad. norm, func.) : 20 1 8.446713182e-09 1.448755140e-08 Iteration (num., iy, grad. norm, func.) : 21 1 1.196851879e-08 1.448503531e-08 Iteration (num., iy, grad. norm, func.) : 22 1 4.054368625e-09 1.447561126e-08 Iteration (num., iy, grad. norm, func.) : 23 1 4.621687218e-09 1.447331134e-08 Iteration (num., iy, grad. norm, func.) : 24 1 3.765764979e-09 1.447105093e-08 Iteration (num., iy, grad. norm, func.) : 25 1 5.334804460e-09 1.446881592e-08 Iteration (num., iy, grad. norm, func.) : 26 1 2.010772855e-09 1.446749825e-08 Iteration (num., iy, grad. norm, func.) : 27 1 3.671998661e-09 1.446677921e-08 Iteration (num., iy, grad. norm, func.) : 28 1 1.631139985e-09 1.446604962e-08 Iteration (num., iy, grad. norm, func.) : 29 1 3.542826070e-09 1.446549400e-08 Iteration (num., iy, grad. norm, func.) : 30 1 9.542027933e-10 1.446452047e-08 Iteration (num., iy, grad. norm, func.) : 31 1 1.188067935e-09 1.446436111e-08 Iteration (num., iy, grad. norm, func.) : 32 1 1.345782379e-09 1.446432277e-08 Iteration (num., iy, grad. norm, func.) : 33 1 1.748189914e-09 1.446430390e-08 Iteration (num., iy, grad. norm, func.) : 34 1 1.263414936e-09 1.446415024e-08 Iteration (num., iy, grad. norm, func.) : 35 1 8.260416831e-10 1.446392531e-08 Iteration (num., iy, grad. norm, func.) : 36 1 6.993097536e-10 1.446379220e-08 Iteration (num., iy, grad. norm, func.) : 37 1 6.442625198e-10 1.446372212e-08 Iteration (num., iy, grad. norm, func.) : 38 1 6.900070799e-10 1.446366233e-08 Iteration (num., iy, grad. norm, func.) : 39 1 3.517489876e-10 1.446362519e-08 Iteration (num., iy, grad. norm, func.) : 40 1 3.111249598e-10 1.446362319e-08 Iteration (num., iy, grad. norm, func.) : 41 1 4.331118092e-10 1.446361597e-08 Iteration (num., iy, grad. norm, func.) : 42 1 2.862868503e-10 1.446359627e-08 Iteration (num., iy, grad. norm, func.) : 43 1 2.555239059e-10 1.446358032e-08 Iteration (num., iy, grad. norm, func.) : 44 1 1.656608986e-10 1.446357153e-08 Iteration (num., iy, grad. norm, func.) : 45 1 1.302415697e-10 1.446357078e-08 Iteration (num., iy, grad. norm, func.) : 46 1 1.384920680e-10 1.446356968e-08 Iteration (num., iy, grad. norm, func.) : 47 1 1.983734872e-10 1.446356687e-08 Iteration (num., iy, grad. norm, func.) : 48 1 1.033395890e-10 1.446356410e-08 Iteration (num., iy, grad. norm, func.) : 49 1 1.783105827e-10 1.446356267e-08 Iteration (num., iy, grad. norm, func.) : 50 1 8.504372891e-11 1.446356183e-08 Iteration (num., iy, grad. norm, func.) : 51 1 4.706446360e-11 1.446356146e-08 Iteration (num., iy, grad. norm, func.) : 52 1 1.055612475e-10 1.446356060e-08 Iteration (num., iy, grad. norm, func.) : 53 1 3.539377179e-11 1.446355980e-08 Iteration (num., iy, grad. norm, func.) : 54 1 3.138627807e-11 1.446355979e-08 Iteration (num., iy, grad. norm, func.) : 55 1 3.353947687e-11 1.446355973e-08 Iteration (num., iy, grad. norm, func.) : 56 1 3.904821974e-11 1.446355954e-08 Iteration (num., iy, grad. norm, func.) : 57 1 4.319046220e-11 1.446355931e-08 Iteration (num., iy, grad. norm, func.) : 58 1 9.981051857e-12 1.446355926e-08 Iteration (num., iy, grad. norm, func.) : 59 1 8.556448373e-12 1.446355926e-08 Iteration (num., iy, grad. norm, func.) : 60 1 1.737515758e-11 1.446355925e-08 Iteration (num., iy, grad. norm, func.) : 61 1 1.143123402e-11 1.446355921e-08 Iteration (num., iy, grad. norm, func.) : 62 1 8.719484755e-12 1.446355918e-08 Iteration (num., iy, grad. norm, func.) : 63 1 8.366045083e-12 1.446355918e-08 Iteration (num., iy, grad. norm, func.) : 64 1 1.213927390e-11 1.446355916e-08 Iteration (num., iy, grad. norm, func.) : 65 1 2.851868503e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 66 1 2.414465887e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 67 1 2.642020366e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 68 1 4.353645960e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 69 1 2.143032098e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 70 1 4.317214961e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 71 1 1.333262874e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 72 1 1.528597611e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 73 1 1.096969027e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 74 1 1.952531148e-12 1.446355915e-08 Iteration (num., iy, grad. norm, func.) : 75 1 6.879998230e-13 1.446355915e-08 Solving for output 1 - done. Time (sec): 0.7740831 Solving nonlinear problem (n=1764) - done. Time (sec): 1.4867001 Solving for degrees of freedom - done. Time (sec): 1.5287228 Training - done. Time (sec): 1.5381517 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002968 Prediction time/pt. (sec) : 0.0000006 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002151 Prediction time/pt. (sec) : 0.0000004 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002499 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002351 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002551 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002429 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002470 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002298 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002649 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002458 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002518 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002520 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002513 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 500 Predicting ... Predicting - done. Time (sec): 0.0002391 Prediction time/pt. (sec) : 0.0000005 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0008912 Prediction time/pt. (sec) : 0.0000004 ___________________________________________________________________________ Evaluation # eval points. : 2500 Predicting ... Predicting - done. Time (sec): 0.0008621 Prediction time/pt. (sec) : 0.0000003 .. figure:: rans_crm_wing.png :scale: 60 % :align: center