Boeing 777 engine data set ========================== .. code-block:: python import os import numpy as np def get_b777_engine(): this_dir = os.path.split(__file__)[0] nt = 12 * 11 * 8 xt = np.loadtxt(os.path.join(this_dir, "b777_engine_inputs.dat")).reshape((nt, 3)) yt = np.loadtxt(os.path.join(this_dir, "b777_engine_outputs.dat")).reshape((nt, 2)) dyt_dxt = np.loadtxt(os.path.join(this_dir, "b777_engine_derivs.dat")).reshape( (nt, 2, 3) ) xlimits = np.array([[0, 0.9], [0, 15], [0, 1.0]]) return xt, yt, dyt_dxt, xlimits def plot_b777_engine(xt, yt, limits, interp): import matplotlib import numpy as np matplotlib.use("Agg") import matplotlib.pyplot as plt val_M = np.array( [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.75, 0.8, 0.85, 0.9] ) # 12 val_h = np.array( [0.0, 0.6096, 1.524, 3.048, 4.572, 6.096, 7.62, 9.144, 10.668, 11.8872, 13.1064] ) # 11 val_t = np.array([0.05, 0.2, 0.3, 0.4, 0.6, 0.8, 0.9, 1.0]) # 8 def get_pts(xt, yt, iy, ind_M=None, ind_h=None, ind_t=None): eps = 1e-5 if ind_M is not None: M = val_M[ind_M] keep = abs(xt[:, 0] - M) < eps xt = xt[keep, :] yt = yt[keep, :] if ind_h is not None: h = val_h[ind_h] keep = abs(xt[:, 1] - h) < eps xt = xt[keep, :] yt = yt[keep, :] if ind_t is not None: t = val_t[ind_t] keep = abs(xt[:, 2] - t) < eps xt = xt[keep, :] yt = yt[keep, :] if ind_M is None: data = xt[:, 0], yt[:, iy] elif ind_h is None: data = xt[:, 1], yt[:, iy] elif ind_t is None: data = xt[:, 2], yt[:, iy] if iy == 0: data = data[0], data[1] / 1e6 elif iy == 1: data = data[0], data[1] / 1e-4 return data num = 100 x = np.zeros((num, 3)) lins_M = np.linspace(0.0, 0.9, num) lins_h = np.linspace(0.0, 13.1064, num) lins_t = np.linspace(0.05, 1.0, num) def get_x(ind_M=None, ind_h=None, ind_t=None): x = np.zeros((num, 3)) x[:, 0] = lins_M x[:, 1] = lins_h x[:, 2] = lins_t if ind_M: x[:, 0] = val_M[ind_M] if ind_h: x[:, 1] = val_h[ind_h] if ind_t: x[:, 2] = val_t[ind_t] return x nrow = 6 ncol = 2 ind_M_1 = -2 ind_M_2 = -5 ind_t_1 = 1 ind_t_2 = -1 plt.close() # -------------------- fig, axs = plt.subplots(nrow, ncol, gridspec_kw={"hspace": 0.5}, figsize=(15, 25)) axs[0, 0].set_title("M={}".format(val_M[ind_M_1])) axs[0, 0].set(xlabel="throttle", ylabel="thrust (x 1e6 N)") axs[0, 1].set_title("M={}".format(val_M[ind_M_1])) axs[0, 1].set(xlabel="throttle", ylabel="SFC (x 1e-3 N/N/s)") axs[1, 0].set_title("M={}".format(val_M[ind_M_2])) axs[1, 0].set(xlabel="throttle", ylabel="thrust (x 1e6 N)") axs[1, 1].set_title("M={}".format(val_M[ind_M_2])) axs[1, 1].set(xlabel="throttle", ylabel="SFC (x 1e-3 N/N/s)") # -------------------- axs[2, 0].set_title("throttle={}".format(val_t[ind_t_1])) axs[2, 0].set(xlabel="altitude (km)", ylabel="thrust (x 1e6 N)") axs[2, 1].set_title("throttle={}".format(val_t[ind_t_1])) axs[2, 1].set(xlabel="altitude (km)", ylabel="SFC (x 1e-3 N/N/s)") axs[3, 0].set_title("throttle={}".format(val_t[ind_t_2])) axs[3, 0].set(xlabel="altitude (km)", ylabel="thrust (x 1e6 N)") axs[3, 1].set_title("throttle={}".format(val_t[ind_t_2])) axs[3, 1].set(xlabel="altitude (km)", ylabel="SFC (x 1e-3 N/N/s)") # -------------------- axs[4, 0].set_title("throttle={}".format(val_t[ind_t_1])) axs[4, 0].set(xlabel="Mach number", ylabel="thrust (x 1e6 N)") axs[4, 1].set_title("throttle={}".format(val_t[ind_t_1])) axs[4, 1].set(xlabel="Mach number", ylabel="SFC (x 1e-3 N/N/s)") axs[5, 0].set_title("throttle={}".format(val_t[ind_t_2])) axs[5, 0].set(xlabel="Mach number", ylabel="thrust (x 1e6 N)") axs[5, 1].set_title("throttle={}".format(val_t[ind_t_2])) axs[5, 1].set(xlabel="Mach number", ylabel="SFC (x 1e-3 N/N/s)") ind_h_list = [0, 4, 7, 10] ind_h_list = [4, 7, 10] ind_M_list = [0, 3, 6, 11] ind_M_list = [3, 6, 11] colors = ["b", "r", "g", "c", "m"] # ----------------------------------------------------------------------------- # Throttle slices for k, ind_h in enumerate(ind_h_list): ind_M = ind_M_1 x = get_x(ind_M=ind_M, ind_h=ind_h) y = interp.predict_values(x) xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_h=ind_h) axs[0, 0].plot(xt_, yt_, "o" + colors[k]) axs[0, 0].plot(lins_t, y[:, 0] / 1e6, colors[k]) xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_h=ind_h) axs[0, 1].plot(xt_, yt_, "o" + colors[k]) axs[0, 1].plot(lins_t, y[:, 1] / 1e-4, colors[k]) ind_M = ind_M_2 x = get_x(ind_M=ind_M, ind_h=ind_h) y = interp.predict_values(x) xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_h=ind_h) axs[1, 0].plot(xt_, yt_, "o" + colors[k]) axs[1, 0].plot(lins_t, y[:, 0] / 1e6, colors[k]) xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_h=ind_h) axs[1, 1].plot(xt_, yt_, "o" + colors[k]) axs[1, 1].plot(lins_t, y[:, 1] / 1e-4, colors[k]) # ----------------------------------------------------------------------------- # Altitude slices for k, ind_M in enumerate(ind_M_list): ind_t = ind_t_1 x = get_x(ind_M=ind_M, ind_t=ind_t) y = interp.predict_values(x) xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_t=ind_t) axs[2, 0].plot(xt_, yt_, "o" + colors[k]) axs[2, 0].plot(lins_h, y[:, 0] / 1e6, colors[k]) xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_t=ind_t) axs[2, 1].plot(xt_, yt_, "o" + colors[k]) axs[2, 1].plot(lins_h, y[:, 1] / 1e-4, colors[k]) ind_t = ind_t_2 x = get_x(ind_M=ind_M, ind_t=ind_t) y = interp.predict_values(x) xt_, yt_ = get_pts(xt, yt, 0, ind_M=ind_M, ind_t=ind_t) axs[3, 0].plot(xt_, yt_, "o" + colors[k]) axs[3, 0].plot(lins_h, y[:, 0] / 1e6, colors[k]) xt_, yt_ = get_pts(xt, yt, 1, ind_M=ind_M, ind_t=ind_t) axs[3, 1].plot(xt_, yt_, "o" + colors[k]) axs[3, 1].plot(lins_h, y[:, 1] / 1e-4, colors[k]) # ----------------------------------------------------------------------------- # Mach number slices for k, ind_h in enumerate(ind_h_list): ind_t = ind_t_1 x = get_x(ind_t=ind_t, ind_h=ind_h) y = interp.predict_values(x) xt_, yt_ = get_pts(xt, yt, 0, ind_h=ind_h, ind_t=ind_t) axs[4, 0].plot(xt_, yt_, "o" + colors[k]) axs[4, 0].plot(lins_M, y[:, 0] / 1e6, colors[k]) xt_, yt_ = get_pts(xt, yt, 1, ind_h=ind_h, ind_t=ind_t) axs[4, 1].plot(xt_, yt_, "o" + colors[k]) axs[4, 1].plot(lins_M, y[:, 1] / 1e-4, colors[k]) ind_t = ind_t_2 x = get_x(ind_t=ind_t, ind_h=ind_h) y = interp.predict_values(x) xt_, yt_ = get_pts(xt, yt, 0, ind_h=ind_h, ind_t=ind_t) axs[5, 0].plot(xt_, yt_, "o" + colors[k]) axs[5, 0].plot(lins_M, y[:, 0] / 1e6, colors[k]) xt_, yt_ = get_pts(xt, yt, 1, ind_h=ind_h, ind_t=ind_t) axs[5, 1].plot(xt_, yt_, "o" + colors[k]) axs[5, 1].plot(lins_M, y[:, 1] / 1e-4, colors[k]) # ----------------------------------------------------------------------------- for k in range(2): legend_entries = [] for ind_h in ind_h_list: legend_entries.append("h={}".format(val_h[ind_h])) legend_entries.append("") axs[k, 0].legend(legend_entries) axs[k, 1].legend(legend_entries) axs[k + 4, 0].legend(legend_entries) axs[k + 4, 1].legend(legend_entries) legend_entries = [] for ind_M in ind_M_list: legend_entries.append("M={}".format(val_M[ind_M])) legend_entries.append("") axs[k + 2, 0].legend(legend_entries) axs[k + 2, 1].legend(legend_entries) plt.show() RMTB ---- .. code-block:: python from smt.examples.b777_engine.b777_engine import get_b777_engine, plot_b777_engine from smt.surrogate_models import RMTB xt, yt, dyt_dxt, xlimits = get_b777_engine() interp = RMTB( num_ctrl_pts=15, xlimits=xlimits, nonlinear_maxiter=20, approx_order=2, energy_weight=0e-14, regularization_weight=0e-18, extrapolate=True, ) interp.set_training_values(xt, yt) interp.set_training_derivatives(xt, dyt_dxt[:, :, 0], 0) interp.set_training_derivatives(xt, dyt_dxt[:, :, 1], 1) interp.set_training_derivatives(xt, dyt_dxt[:, :, 2], 2) interp.train() plot_b777_engine(xt, yt, xlimits, interp) :: ___________________________________________________________________________ RMTB ___________________________________________________________________________ Problem size # training points. : 1056 ___________________________________________________________________________ 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): 0.2293053 Computing approximation terms ... Computing approximation terms - done. Time (sec): 0.0075541 Pre-computing matrices - done. Time (sec): 0.2368593 Solving for degrees of freedom ... Solving initial startup problem (n=3375) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 4.857178281e+07 2.642628384e+13 Iteration (num., iy, grad. norm, func.) : 0 0 1.373826973e+05 6.997915387e+09 Solving for output 0 - done. Time (sec): 0.0675280 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 3.711896708e-01 7.697335516e-04 Iteration (num., iy, grad. norm, func.) : 0 1 1.395061018e-03 3.468699832e-07 Solving for output 1 - done. Time (sec): 0.0736167 Solving initial startup problem (n=3375) - done. Time (sec): 0.1416497 Solving nonlinear problem (n=3375) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 1.373826973e+05 6.997915387e+09 Iteration (num., iy, grad. norm, func.) : 0 0 7.235665692e+04 1.954806038e+09 Iteration (num., iy, grad. norm, func.) : 1 0 4.654098320e+04 5.658756761e+08 Iteration (num., iy, grad. norm, func.) : 2 0 3.672346133e+04 3.885491673e+08 Iteration (num., iy, grad. norm, func.) : 3 0 3.261616842e+04 3.768480084e+08 Iteration (num., iy, grad. norm, func.) : 4 0 2.686026706e+04 3.249773050e+08 Iteration (num., iy, grad. norm, func.) : 5 0 1.626148419e+04 2.983960747e+08 Iteration (num., iy, grad. norm, func.) : 6 0 1.524365745e+04 2.654419506e+08 Iteration (num., iy, grad. norm, func.) : 7 0 8.490561347e+03 2.216463966e+08 Iteration (num., iy, grad. norm, func.) : 8 0 9.545883104e+03 2.016764770e+08 Iteration (num., iy, grad. norm, func.) : 9 0 5.720345076e+03 1.864751429e+08 Iteration (num., iy, grad. norm, func.) : 10 0 8.662166329e+03 1.767845928e+08 Iteration (num., iy, grad. norm, func.) : 11 0 6.197316018e+03 1.659797529e+08 Iteration (num., iy, grad. norm, func.) : 12 0 4.224243819e+03 1.618341373e+08 Iteration (num., iy, grad. norm, func.) : 13 0 3.112670522e+03 1.600496853e+08 Iteration (num., iy, grad. norm, func.) : 14 0 4.370148466e+03 1.600262496e+08 Iteration (num., iy, grad. norm, func.) : 15 0 2.859520501e+03 1.569733173e+08 Iteration (num., iy, grad. norm, func.) : 16 0 2.782479646e+03 1.533014054e+08 Iteration (num., iy, grad. norm, func.) : 17 0 2.299670974e+03 1.496565883e+08 Iteration (num., iy, grad. norm, func.) : 18 0 1.610566561e+03 1.487769054e+08 Iteration (num., iy, grad. norm, func.) : 19 0 1.447300133e+03 1.485878967e+08 Solving for output 0 - done. Time (sec): 1.2917762 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 1.395061018e-03 3.468699832e-07 Iteration (num., iy, grad. norm, func.) : 0 1 3.914872455e-04 6.182312112e-08 Iteration (num., iy, grad. norm, func.) : 1 1 2.865874329e-04 1.805178232e-08 Iteration (num., iy, grad. norm, func.) : 2 1 2.247617079e-04 8.253323304e-09 Iteration (num., iy, grad. norm, func.) : 3 1 1.768547031e-04 7.596092771e-09 Iteration (num., iy, grad. norm, func.) : 4 1 1.254616429e-04 6.616975834e-09 Iteration (num., iy, grad. norm, func.) : 5 1 1.014353342e-04 5.078978077e-09 Iteration (num., iy, grad. norm, func.) : 6 1 4.561000928e-05 2.963531897e-09 Iteration (num., iy, grad. norm, func.) : 7 1 6.361066346e-05 2.080802088e-09 Iteration (num., iy, grad. norm, func.) : 8 1 2.006390508e-05 1.779385831e-09 Iteration (num., iy, grad. norm, func.) : 9 1 1.934234213e-05 1.701506695e-09 Iteration (num., iy, grad. norm, func.) : 10 1 2.059391901e-05 1.612436453e-09 Iteration (num., iy, grad. norm, func.) : 11 1 2.588418235e-05 1.449071076e-09 Iteration (num., iy, grad. norm, func.) : 12 1 1.072301170e-05 1.307094325e-09 Iteration (num., iy, grad. norm, func.) : 13 1 2.014181444e-05 1.265540786e-09 Iteration (num., iy, grad. norm, func.) : 14 1 1.119759769e-05 1.250124472e-09 Iteration (num., iy, grad. norm, func.) : 15 1 1.353578802e-05 1.231299250e-09 Iteration (num., iy, grad. norm, func.) : 16 1 1.274579638e-05 1.185742022e-09 Iteration (num., iy, grad. norm, func.) : 17 1 9.960075664e-06 1.162834806e-09 Iteration (num., iy, grad. norm, func.) : 18 1 5.406897757e-06 1.149088640e-09 Iteration (num., iy, grad. norm, func.) : 19 1 8.596626113e-06 1.145548481e-09 Solving for output 1 - done. Time (sec): 1.1725609 Solving nonlinear problem (n=3375) - done. Time (sec): 2.4643371 Solving for degrees of freedom - done. Time (sec): 2.6059868 Training - done. Time (sec): 2.8438473 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010023 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010002 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010002 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0009992 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0009971 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0011530 Prediction time/pt. (sec) : 0.0000115 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010028 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010006 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0009995 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010352 Prediction time/pt. (sec) : 0.0000104 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010073 Prediction time/pt. (sec) : 0.0000101 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010004 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0009961 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0000000 Prediction time/pt. (sec) : 0.0000000 .. figure:: b777_engine.png :scale: 60 % :align: center RMTC ---- .. code-block:: python from smt.examples.b777_engine.b777_engine import get_b777_engine, plot_b777_engine from smt.surrogate_models import RMTC xt, yt, dyt_dxt, xlimits = get_b777_engine() interp = RMTC( num_elements=6, xlimits=xlimits, nonlinear_maxiter=20, approx_order=2, energy_weight=0.0, regularization_weight=0.0, extrapolate=True, ) interp.set_training_values(xt, yt) interp.set_training_derivatives(xt, dyt_dxt[:, :, 0], 0) interp.set_training_derivatives(xt, dyt_dxt[:, :, 1], 1) interp.set_training_derivatives(xt, dyt_dxt[:, :, 2], 2) interp.train() plot_b777_engine(xt, yt, xlimits, interp) :: ___________________________________________________________________________ RMTC ___________________________________________________________________________ Problem size # training points. : 1056 ___________________________________________________________________________ Training Training ... Pre-computing matrices ... Computing dof2coeff ... Computing dof2coeff - done. Time (sec): 0.0221553 Initializing Hessian ... Initializing Hessian - done. Time (sec): 0.0009987 Computing energy terms ... Computing energy terms - done. Time (sec): 0.1575251 Computing approximation terms ... Computing approximation terms - done. Time (sec): 0.0562134 Pre-computing matrices - done. Time (sec): 0.2368925 Solving for degrees of freedom ... Solving initial startup problem (n=2744) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 7.864862172e+07 2.642628384e+13 Iteration (num., iy, grad. norm, func.) : 0 0 2.013823241e+05 2.067229908e+09 Solving for output 0 - done. Time (sec): 0.1890025 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 8.095040141e-01 7.697335516e-04 Iteration (num., iy, grad. norm, func.) : 0 1 1.423039559e-03 1.317122164e-07 Solving for output 1 - done. Time (sec): 0.2434375 Solving initial startup problem (n=2744) - done. Time (sec): 0.4324400 Solving nonlinear problem (n=2744) ... Solving for output 0 ... Iteration (num., iy, grad. norm, func.) : 0 0 2.013823241e+05 2.067229908e+09 Iteration (num., iy, grad. norm, func.) : 0 0 3.142149731e+04 4.216257744e+08 Iteration (num., iy, grad. norm, func.) : 1 0 1.628819259e+04 3.528261743e+08 Iteration (num., iy, grad. norm, func.) : 2 0 2.431474045e+04 3.502779731e+08 Iteration (num., iy, grad. norm, func.) : 3 0 9.979393989e+03 3.372917623e+08 Iteration (num., iy, grad. norm, func.) : 4 0 4.504412583e+03 3.327215706e+08 Iteration (num., iy, grad. norm, func.) : 5 0 5.742398374e+03 3.320226809e+08 Iteration (num., iy, grad. norm, func.) : 6 0 2.634482225e+03 3.312305682e+08 Iteration (num., iy, grad. norm, func.) : 7 0 2.027481346e+03 3.307021257e+08 Iteration (num., iy, grad. norm, func.) : 8 0 1.164812990e+03 3.304671562e+08 Iteration (num., iy, grad. norm, func.) : 9 0 2.056698510e+03 3.303603150e+08 Iteration (num., iy, grad. norm, func.) : 10 0 1.588700733e+03 3.302176992e+08 Iteration (num., iy, grad. norm, func.) : 11 0 1.417683539e+03 3.301320906e+08 Iteration (num., iy, grad. norm, func.) : 12 0 8.464194882e+02 3.300058274e+08 Iteration (num., iy, grad. norm, func.) : 13 0 1.469318435e+03 3.299022133e+08 Iteration (num., iy, grad. norm, func.) : 14 0 3.776511399e+02 3.298368605e+08 Iteration (num., iy, grad. norm, func.) : 15 0 5.774489399e+02 3.298363026e+08 Iteration (num., iy, grad. norm, func.) : 16 0 4.650683101e+02 3.298262578e+08 Iteration (num., iy, grad. norm, func.) : 17 0 6.478920533e+02 3.298143199e+08 Iteration (num., iy, grad. norm, func.) : 18 0 3.655761629e+02 3.297990853e+08 Iteration (num., iy, grad. norm, func.) : 19 0 2.635230494e+02 3.297943728e+08 Solving for output 0 - done. Time (sec): 2.9683211 Solving for output 1 ... Iteration (num., iy, grad. norm, func.) : 0 1 1.423039559e-03 1.317122164e-07 Iteration (num., iy, grad. norm, func.) : 0 1 4.801149326e-04 9.499902244e-09 Iteration (num., iy, grad. norm, func.) : 1 1 2.806984163e-04 7.828673651e-09 Iteration (num., iy, grad. norm, func.) : 2 1 2.607850877e-04 6.049600013e-09 Iteration (num., iy, grad. norm, func.) : 3 1 9.397660769e-05 4.307701931e-09 Iteration (num., iy, grad. norm, func.) : 4 1 8.401306727e-05 4.061384351e-09 Iteration (num., iy, grad. norm, func.) : 5 1 8.858356348e-05 3.737740437e-09 Iteration (num., iy, grad. norm, func.) : 6 1 4.418680845e-05 3.360984713e-09 Iteration (num., iy, grad. norm, func.) : 7 1 4.194077635e-05 3.204942422e-09 Iteration (num., iy, grad. norm, func.) : 8 1 5.270100036e-05 3.122969531e-09 Iteration (num., iy, grad. norm, func.) : 9 1 2.806077101e-05 3.065922721e-09 Iteration (num., iy, grad. norm, func.) : 10 1 2.302063128e-05 3.043676807e-09 Iteration (num., iy, grad. norm, func.) : 11 1 3.443713096e-05 3.035276555e-09 Iteration (num., iy, grad. norm, func.) : 12 1 2.187733093e-05 3.018521234e-09 Iteration (num., iy, grad. norm, func.) : 13 1 1.908237935e-05 2.990285088e-09 Iteration (num., iy, grad. norm, func.) : 14 1 1.556075444e-05 2.957239892e-09 Iteration (num., iy, grad. norm, func.) : 15 1 1.298701588e-05 2.936695843e-09 Iteration (num., iy, grad. norm, func.) : 16 1 8.194155021e-06 2.928570971e-09 Iteration (num., iy, grad. norm, func.) : 17 1 9.208864453e-06 2.925633327e-09 Iteration (num., iy, grad. norm, func.) : 18 1 7.771173950e-06 2.923701376e-09 Iteration (num., iy, grad. norm, func.) : 19 1 1.062166843e-05 2.920674153e-09 Solving for output 1 - done. Time (sec): 2.7008305 Solving nonlinear problem (n=2744) - done. Time (sec): 5.6691515 Solving for degrees of freedom - done. Time (sec): 6.1015916 Training - done. Time (sec): 6.3395131 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010016 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0019357 Prediction time/pt. (sec) : 0.0000194 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0019703 Prediction time/pt. (sec) : 0.0000197 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0019977 Prediction time/pt. (sec) : 0.0000200 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010009 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010037 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0019994 Prediction time/pt. (sec) : 0.0000200 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010023 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010011 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0020323 Prediction time/pt. (sec) : 0.0000203 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010257 Prediction time/pt. (sec) : 0.0000103 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0020003 Prediction time/pt. (sec) : 0.0000200 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0009995 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010004 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0019758 Prediction time/pt. (sec) : 0.0000198 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0010316 Prediction time/pt. (sec) : 0.0000103 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0009997 Prediction time/pt. (sec) : 0.0000100 ___________________________________________________________________________ Evaluation # eval points. : 100 Predicting ... Predicting - done. Time (sec): 0.0019929 Prediction time/pt. (sec) : 0.0000199 .. figure:: b777_engine.png :scale: 60 % :align: center