From d81ddcbffd8690fff504d584a252b43e5a12ac6d Mon Sep 17 00:00:00 2001 From: "Nagrani, Pranay Praveen" Date: Mon, 10 Apr 2023 11:07:55 -0400 Subject: [PATCH] Add files via upload --- .../rheology_TIMs-TEVP-DOWTC5622-Final.ipynb | 3734 +++++++++++++++++ 1 file changed, 3734 insertions(+) create mode 100644 DOWSIL TC-5622/rheology_TIMs-TEVP-DOWTC5622-Final.ipynb diff --git a/DOWSIL TC-5622/rheology_TIMs-TEVP-DOWTC5622-Final.ipynb b/DOWSIL TC-5622/rheology_TIMs-TEVP-DOWTC5622-Final.ipynb new file mode 100644 index 0000000..6199e89 --- /dev/null +++ b/DOWSIL TC-5622/rheology_TIMs-TEVP-DOWTC5622-Final.ipynb @@ -0,0 +1,3734 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "id": "2f3d6c3f", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.7.0\n" + ] + } + ], + "source": [ + "# Loading all Modules #\n", + "import numpy as np\n", + "import deepxde as dde\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "from deepxde.backend import tf\n", + "from scipy import interpolate\n", + "print(tf.__version__)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bb66e151", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Setting non-dimensional parameters based on experimental data #\n", + "\n", + "del_stress = 650.0-100.0 #1600\n", + "stress_min = 100.0 \n", + "del_time = 9.858-0.1\n", + "time_min = 0.1\n", + "del_shear_rate = 10.0-1.9\n", + "shear_min = 1.9 \n", + "\n", + "# Generating training data by reading excel files #\n", + "\n", + "def gen_training_data():\n", + " \n", + " time_data = []\n", + " gammadot_data = []\n", + " shear_stress_data = []\n", + " \n", + " for itr in range(1,10): \n", + " if itr==6: # skipping shear rate of 7/s from giving as training data so the prediction can be checked later\n", + " continue\n", + " t_expt = []\n", + " t_expt_first = []\n", + " t_expt_last = []\n", + " gammadot = []\n", + " gammadot_first = []\n", + " gammadot_last = []\n", + " shear_stress = []\n", + " shear_stress_first = []\n", + " shear_stress_last = []\n", + " t_fitted = []\n", + " gammadot_fitted = []\n", + " shear_stress_fitted = []\n", + " \n", + " startUpFlow = pd.read_excel('DOWTC5622-09-02-22.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=itr, skiprows=range(148,296)) #148\n", + " # Reading data from sheet number - itr\n", + " t_expt = np.array(startUpFlow[\"t\"])\n", + " t_expt_first = t_expt[0:48]\n", + " t_expt_last = t_expt[48:]\n", + " print(t_expt_first)\n", + " print(t_expt_last)\n", + " \n", + " #print(t_expt)\n", + " gammadot = np.array(startUpFlow[\"shear rate\"])\n", + " gammadot_first = gammadot[0:48]\n", + " gammadot_last = gammadot[48:]\n", + " #gammadot = gammadot[:,np.newaxis]\n", + " \n", + " shear_stress = np.array(startUpFlow[\"shear stress\"])\n", + " shear_stress_first = shear_stress[0:48]\n", + " shear_stress_last = shear_stress[48:]\n", + " #shear_stress = shear_stress[:,np.newaxis]\n", + " \n", + " # interpolating shear stress and shear rate separately with time (increasing data points)\n", + " f_shear_stress = interpolate.interp1d(t_expt_first, shear_stress_first) \n", + " f_gammadot = interpolate.interp1d(t_expt_first, gammadot_first)\n", + " t_fitted = np.linspace(t_expt_first[0],t_expt_first[t_expt_first.size-1],250) \n", + " gammadot_fitted = f_gammadot(t_fitted)\n", + " shear_stress_fitted = f_shear_stress(t_fitted) \n", + " \n", + " f_shear_stress_1 = interpolate.interp1d(t_expt_last, shear_stress_last) \n", + " f_gammadot_1 = interpolate.interp1d(t_expt_last, gammadot_last)\n", + " t_fitted_1 = np.linspace(t_expt_last[0],t_expt_last[t_expt_last.size-1],300) \n", + " gammadot_fitted_1 = f_gammadot_1(t_fitted_1)\n", + " shear_stress_fitted_1 = f_shear_stress_1(t_fitted_1)\n", + " \n", + " time_itr = []\n", + " gammadot_itr = []\n", + " shear_stress_itr = []\n", + " \n", + " time_itr = np.append(t_fitted,t_fitted_1)\n", + " time_itr = time_itr[:,np.newaxis]\n", + " \n", + " gammadot_itr = np.append(gammadot_fitted,gammadot_fitted_1)\n", + " gammadot_itr = gammadot_itr[:,np.newaxis] \n", + " \n", + " shear_stress_itr = np.append(shear_stress_fitted,shear_stress_fitted_1)\n", + " shear_stress_itr = shear_stress_itr[:,np.newaxis]\n", + " \n", + " plt.plot(time_itr,shear_stress_itr, '-r', label='Fit')\n", + " plt.plot(t_expt,shear_stress,'*b', label='Experiment')\n", + " plt.legend()\n", + " plt.xscale('log')\n", + "\n", + " plt.xlabel('time (s)')\n", + " plt.ylabel('Shear stress (Pa)')\n", + " plt.show()\n", + " \n", + " # Non-Dimensionalizing - O(0-1)\n", + " time_itr = (time_itr-time_min)/(del_time) \n", + " gammadot_itr = (gammadot_itr-shear_min)/(del_shear_rate) \n", + " shear_stress_itr = (shear_stress_itr-stress_min)/(del_stress) \n", + " \n", + " # Appending data from each sheet to a global array and converting to a column\n", + " \n", + " time_data = np.append(time_data,time_itr)\n", + " time_data = time_data[:,np.newaxis]\n", + " \n", + " gammadot_data = np.append(gammadot_data,gammadot_itr)\n", + " gammadot_data = gammadot_data[:,np.newaxis] \n", + " \n", + " shear_stress_data = np.append(shear_stress_data,shear_stress_itr)\n", + " shear_stress_data = shear_stress_data[:,np.newaxis]\n", + " \n", + " print(np.size(gammadot_data))\n", + " print(np.size(time_data)) \n", + " print(np.size(shear_stress_data))\n", + " #print(gammadot_data)\n", + " #print(time_data)\n", + " \n", + " \n", + " return np.hstack((gammadot_data, time_data)) , shear_stress_data \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "0eafd147", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0.11 0.122 0.134 0.147 0.159 0.173 0.186 0.199 0.213 0.227 0.242 0.256\n", + " 0.271 0.286 0.302 0.318 0.334 0.35 0.367 0.384 0.402 0.419 0.437 0.456\n", + " 0.474 0.494 0.513 0.533 0.553 0.574 0.595 0.616 0.638 0.661 0.683 0.706\n", + " 0.73 0.754 0.779 0.804 0.829 0.855 0.881 0.908 0.936 0.964 0.992 1.022]\n", + "[1.051 1.081 1.112 1.144 1.176 1.208 1.242 1.275 1.31 1.345 1.381 1.418\n", + " 1.455 1.493 1.532 1.571 1.611 1.653 1.694 1.737 1.78 1.825 1.87 1.916\n", + " 1.963 2.011 2.059 2.109 2.159 2.211 2.264 2.317 2.372 2.427 2.484 2.542\n", + " 2.601 2.661 2.722 2.785 2.848 2.913 2.979 3.047 3.115 3.185 3.257 3.329\n", + " 3.404 3.479 3.556 3.635 3.715 3.796 3.879 3.964 4.05 4.138 4.228 4.319\n", + " 4.412 4.507 4.604 4.703 4.803 4.906 5.01 5.117 5.225 5.336 5.449 5.564\n", + " 5.681 5.8 5.922 6.046 6.173 6.301 6.433 6.567 6.703 6.842 6.984 7.128\n", + " 7.276 7.426 7.579 7.735 7.894 8.056 8.221 8.389 8.561 8.736 8.914 9.096\n", + " 9.281 9.469 9.662 9.858]\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "4400\n", + "4400\n", + "4400\n", + "[[0.01358025 0.0010248 ]\n", + " [0.01358025 0.00140015]\n", + " [0.01358025 0.0017755 ]\n", + " ...\n", + " [1. 0.99396293]\n", + " [1. 0.99698147]\n", + " [1. 1. ]]\n", + "Warning: 5000 points required, but 5041 points sampled.\n" + ] + } + ], + "source": [ + "# time interval and shear interval of interest\n", + "timedomain = dde.geometry.TimeDomain(0,1) # non-dimensional end time\n", + "geom = dde.geometry.Interval(0,1) # non-dimensional shear rate\n", + "timexgeom = dde.geometry.GeometryXTime(geom, timedomain) # Creating time and geometry fields\n", + "\n", + "# defining and initializing training model paramaters (to be determined by solving inverse problem)\n", + "G = dde.Variable(80.0) # 160 80.0\n", + "eta_s = dde.Variable(44.5) # 100 44.5\n", + "k_plus = dde.Variable(1.0) # 0.5 1.0\n", + "k_minus = dde.Variable(1.0) # 0.5 1.0\n", + "eta_p = dde.Variable(50.0)#50.0\n", + "yield_stress = dde.Variable(2.0)\n", + "\n", + "\n", + "# defining the TEVP rheology model\n", + "def pde(x,y):\n", + " \n", + " stress, struct_param = y[:,0:1], y[:,1:2]\n", + " shear_rate, t = x[:,0:1], x[:,1:2]\n", + " \n", + " stress_rate = dde.grad.jacobian(y,x,i=0,j=1)\n", + " struct_rate = dde.grad.jacobian(y,x,i=1,j=1)\n", + " \n", + " eqn1 = stress_rate - (G*del_time)/((eta_s+eta_p)*del_stress)* \\\n", + " (-stress*del_stress - stress_min + yield_stress*struct_param \\\n", + " + (eta_s + eta_p*struct_param)*(shear_rate*del_shear_rate+shear_min)) # removed exponent n \n", + " \n", + " eqn2 = struct_rate - del_time*(k_plus*(1-struct_param) - k_minus*struct_param*(shear_rate*del_shear_rate+shear_min))\n", + " \n", + " return [eqn1,eqn2]\n", + "\n", + "\n", + "# training data\n", + "observe_x, shear_stress_training = gen_training_data() # observe_x gets shear rate and time \n", + "print(observe_x)\n", + "observe_y1 = dde.PointSetBC(observe_x, shear_stress_training, component=0) \n", + "# No observe_y2 as structure parameter not including in training data\n", + "\n", + "# defining the data variable\n", + "data = dde.data.TimePDE(\n", + " timexgeom,\n", + " pde,\n", + " [observe_y1],\n", + " num_domain = 3000, \n", + " anchors = observe_x,\n", + " num_test = 5000, \n", + " train_distribution='pseudo', \n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3b0f8e70", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "layer_size = [2] + [10]*4 + [2] # NN architecture \n", + "activation = \"tanh\" # activation function\n", + "initializer = \"Glorot normal\" # Xavier initialization\n", + "\n", + "# developing the Neural network\n", + "net = dde.nn.FNN(layer_size, activation, initializer)\n", + "\n", + "# Output constraint to ensure structure parameter is between 0 and 1\n", + "def output_transform(x,y):\n", + " return tf.concat((y[:,0:1], tf.sigmoid(y[:,1:2])), axis=1)\n", + " \n", + "\n", + "net.apply_output_transform(output_transform)\n", + "\n", + "# developing the model\n", + "model = dde.Model(data,net)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2de7b943", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling model...\n", + "Warning: For the backend tensorflow.compat.v1, `external_trainable_variables` is ignored, and all trainable ``tf.Variable`` objects are automatically collected.\n", + "Building feed-forward neural network...\n", + "'build' took 0.110927 s\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\deepxde\\nn\\tensorflow_compat_v1\\fnn.py:110: UserWarning: `tf.layers.dense` is deprecated and will be removed in a future version. Please use `tf.keras.layers.Dense` instead.\n", + " kernel_constraint=self.kernel_constraint,\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\keras\\legacy_tf_layers\\core.py:255: UserWarning: `layer.apply` is deprecated and will be removed in a future version. Please use `layer.__call__` method instead.\n", + " return layer.apply(inputs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "'compile' took 0.848711 s\n", + "\n" + ] + } + ], + "source": [ + "# compiling the NN\n", + "model.compile(\"adam\",\n", + " lr=0.001, # learning rate\n", + " loss_weights=[1,1,1e1], # w_sigma, w_lambda, w_data\n", + " external_trainable_variables=[G, eta_s, k_plus, k_minus, eta_p, yield_stress]) # Unknown model parameters to be determined\n", + "\n", + "# extracting training parameters value any intermediate intervals and saving the file\n", + "variable = dde.callbacks.VariableValue([G, eta_s, k_plus, k_minus, eta_p, yield_stress], \n", + " period=1000, # Show output every 1000 epochs\n", + " filename=\"variables_DOWTC5622_TEVP3_7s.dat\") #File name where the parameters are stored\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "17391d5e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing variables...\n", + "Training model...\n", + "\n", + "Step Train loss Test loss Test metric\n", + "0 [1.79e+01, 4.49e+02, 7.28e-01] [2.07e+01, 4.92e+02, 7.28e-01] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1000 [7.47e-02, 5.42e-02, 7.12e-02] [5.77e-02, 4.76e-02, 7.12e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "2000 [3.12e-02, 3.77e-02, 6.56e-02] [2.40e-02, 3.30e-02, 6.56e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-2000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "3000 [1.28e-02, 2.94e-02, 6.05e-02] [8.79e-03, 2.51e-02, 6.05e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-3000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "4000 [1.16e-02, 2.20e-02, 5.27e-02] [6.91e-03, 1.81e-02, 5.27e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-4000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "5000 [1.42e-02, 1.48e-02, 4.61e-02] [9.48e-03, 1.18e-02, 4.61e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-5000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "6000 [1.33e-02, 6.50e-03, 4.38e-02] [8.97e-03, 5.17e-03, 4.38e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-6000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "7000 [1.28e-02, 1.14e-03, 3.91e-02] [8.80e-03, 8.62e-04, 3.91e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-7000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "8000 [9.26e-03, 1.04e-03, 2.39e-02] [6.64e-03, 9.75e-04, 2.39e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-8000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "9000 [2.38e-03, 1.24e-03, 1.18e-02] [1.97e-03, 1.39e-03, 1.18e-02] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-9000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "10000 [1.59e-03, 1.06e-03, 7.09e-03] [1.50e-03, 1.33e-03, 7.09e-03] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-10000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "11000 [1.24e-03, 8.47e-04, 5.81e-03] [1.05e-03, 1.07e-03, 5.81e-03] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-11000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "12000 [4.82e-04, 2.58e-04, 5.48e-03] [3.71e-04, 3.01e-04, 5.48e-03] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-12000.ckpt is not in all_model_checkpoint_paths. 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Manually adding it.\n", + "1452000 [1.97e-05, 6.03e-06, 5.85e-04] [1.74e-05, 6.29e-06, 5.85e-04] [] \n", + "1453000 [6.04e-06, 6.15e-06, 5.85e-04] [4.38e-06, 6.15e-06, 5.85e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1453000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1454000 [1.00e-04, 8.76e-06, 5.87e-04] [1.03e-04, 7.89e-06, 5.87e-04] [] \n", + "1455000 [2.15e-05, 6.30e-06, 5.85e-04] [1.84e-05, 6.21e-06, 5.85e-04] [] \n", + "1456000 [1.81e-03, 1.17e-04, 6.02e-04] [1.88e-03, 9.21e-05, 6.02e-04] [] \n", + "1457000 [2.94e-03, 2.23e-05, 6.19e-04] [2.88e-03, 1.87e-05, 6.19e-04] [] \n", + "1458000 [6.53e-06, 6.25e-06, 5.84e-04] [4.41e-06, 6.25e-06, 5.84e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1458000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1459000 [1.11e-04, 6.75e-06, 5.85e-04] [1.05e-04, 6.67e-06, 5.85e-04] [] \n", + "1460000 [3.27e-04, 1.39e-05, 5.87e-04] [3.32e-04, 1.23e-05, 5.87e-04] [] \n", + "1461000 [7.78e-06, 6.55e-06, 5.83e-04] [5.84e-06, 6.18e-06, 5.83e-04] [] \n", + "1462000 [5.65e-06, 6.22e-06, 5.83e-04] [4.39e-06, 6.05e-06, 5.83e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1462000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1463000 [6.01e-06, 6.37e-06, 5.83e-04] [4.45e-06, 6.09e-06, 5.83e-04] [] \n", + "1464000 [6.10e-06, 6.18e-06, 5.83e-04] [4.51e-06, 6.15e-06, 5.83e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1464000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1465000 [4.63e-04, 6.31e-06, 5.89e-04] [4.33e-04, 6.19e-06, 5.89e-04] [] \n", + "1466000 [5.70e-06, 6.02e-06, 5.83e-04] [4.46e-06, 6.22e-06, 5.83e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1466000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1467000 [5.78e-06, 6.15e-06, 5.83e-04] [4.31e-06, 6.03e-06, 5.83e-04] [] \n", + "1468000 [5.82e-06, 6.21e-06, 5.82e-04] [4.51e-06, 6.23e-06, 5.82e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1468000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1469000 [5.92e-06, 6.81e-06, 5.82e-04] [4.29e-06, 6.61e-06, 5.82e-04] [] \n", + "1470000 [1.17e-04, 7.11e-06, 5.84e-04] [1.04e-04, 6.81e-06, 5.84e-04] [] \n", + "1471000 [8.28e-06, 6.79e-06, 5.82e-04] [7.26e-06, 6.77e-06, 5.82e-04] [] \n", + "1472000 [6.04e-06, 6.21e-06, 5.81e-04] [4.44e-06, 6.22e-06, 5.81e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1472000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1473000 [1.56e-04, 7.51e-06, 5.84e-04] [1.55e-04, 7.18e-06, 5.84e-04] [] \n", + "1474000 [1.02e-05, 8.33e-06, 5.82e-04] [6.56e-06, 7.73e-06, 5.82e-04] [] \n", + "1475000 [6.45e-06, 6.24e-06, 5.81e-04] [5.19e-06, 6.24e-06, 5.81e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1475000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1476000 [6.01e-06, 6.20e-06, 5.81e-04] [4.78e-06, 6.00e-06, 5.81e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1476000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1477000 [3.63e-05, 6.08e-06, 5.81e-04] [3.20e-05, 6.19e-06, 5.81e-04] [] \n", + "1478000 [1.04e-05, 7.10e-06, 5.80e-04] [9.44e-06, 7.01e-06, 5.80e-04] [] \n", + "1479000 [5.85e-06, 6.21e-06, 5.80e-04] [4.36e-06, 6.27e-06, 5.80e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1479000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1480000 [1.12e-05, 6.31e-06, 5.80e-04] [9.76e-06, 6.32e-06, 5.80e-04] [] \n", + "1481000 [5.84e-06, 6.14e-06, 5.80e-04] [4.42e-06, 6.19e-06, 5.80e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1481000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1482000 [1.76e-04, 7.93e-06, 5.82e-04] [1.75e-04, 7.63e-06, 5.82e-04] [] \n", + "1483000 [7.67e-04, 1.28e-05, 5.89e-04] [7.64e-04, 1.17e-05, 5.89e-04] [] \n", + "1484000 [2.47e-05, 1.39e-05, 5.79e-04] [1.76e-05, 1.27e-05, 5.79e-04] [] \n", + "1485000 [2.25e-05, 6.52e-06, 5.79e-04] [2.10e-05, 6.29e-06, 5.79e-04] [] \n", + "1486000 [3.59e-05, 7.67e-06, 5.79e-04] [3.42e-05, 7.31e-06, 5.79e-04] [] \n", + "1487000 [6.21e-06, 6.35e-06, 5.79e-04] [4.87e-06, 6.26e-06, 5.79e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1487000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1488000 [6.13e-05, 6.66e-06, 5.80e-04] [6.03e-05, 6.43e-06, 5.80e-04] [] \n", + "1489000 [5.90e-06, 6.33e-06, 5.78e-04] [4.39e-06, 6.20e-06, 5.78e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1489000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1490000 [6.43e-06, 6.21e-06, 5.78e-04] [4.90e-06, 6.22e-06, 5.78e-04] [] \n", + "1491000 [5.82e-06, 6.23e-06, 5.78e-04] [4.38e-06, 6.25e-06, 5.78e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1491000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1492000 [6.45e-06, 6.55e-06, 5.78e-04] [4.83e-06, 6.18e-06, 5.78e-04] [] \n", + "1493000 [8.81e-06, 6.32e-06, 5.78e-04] [7.03e-06, 6.29e-06, 5.78e-04] [] \n", + "1494000 [1.58e-05, 7.02e-06, 5.78e-04] [1.55e-05, 6.78e-06, 5.78e-04] [] \n", + "1495000 [8.95e-06, 7.11e-06, 5.77e-04] [5.71e-06, 6.66e-06, 5.77e-04] [] \n", + "1496000 [5.65e-06, 6.30e-06, 5.77e-04] [4.30e-06, 6.13e-06, 5.77e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1496000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1497000 [5.87e-06, 6.47e-06, 5.77e-04] [4.40e-06, 6.12e-06, 5.77e-04] [] \n", + "1498000 [5.92e-06, 6.27e-06, 5.77e-04] [4.23e-06, 6.05e-06, 5.77e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1498000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1499000 [5.72e-06, 6.32e-06, 5.77e-04] [4.33e-06, 6.19e-06, 5.77e-04] [] \n", + "INFO:tensorflow:model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1499000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "1500000 [3.55e-04, 8.15e-06, 5.81e-04] [3.47e-04, 7.74e-06, 5.81e-04] [] \n", + "\n", + "Best model at step 1499000:\n", + " train loss: 5.89e-04\n", + " test loss: 5.87e-04\n", + " test metric: []\n", + "\n", + "INFO:tensorflow:C/model-DOWTC5622-TEVP-final3-7s-1500000.ckpt is not in all_model_checkpoint_paths. Manually adding it.\n", + "Epoch 1500000: saving model to C/model-DOWTC5622-TEVP-final3-7s-1500000.ckpt ...\n", + "\n", + "'train' took 20051.606449 s\n", + "\n", + "Saving loss history to C:\\Users\\pnagrani\\loss.dat ...\n", + "Saving training data to C:\\Users\\pnagrani\\train.dat ...\n", + "Saving test data to C:\\Users\\pnagrani\\test.dat ...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Saving model at intermediate steps #\n", + "checkpointer = dde.callbacks.ModelCheckpoint(\n", + " \"model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3\", verbose=0, save_better_only=True\n", + " )\n", + "\n", + "# Resampling PDE loss data points #\n", + "resampler = dde.callbacks.PDEResidualResampler(period=1000)\n", + "\n", + "# Training the model #\n", + "losshistory, trainstate = model.train(epochs = 1500000, callbacks = [variable, checkpointer, resampler],model_save_path='C/model-DOWTC5622-TEVP-final3-7s') \n", + "\n", + "# Saving train and test error plots #\n", + "dde.saveplot(losshistory, trainstate, issave=True, isplot=True)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ef422396", + "metadata": {}, + "outputs": [], + "source": [ + "# experimental data to validate the model results at unseen 7/s data #\n", + "\n", + "valPoints = pd.read_excel('DOWTC5622-09-02-22.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=6,skiprows=range(148,296))\n", + "\n", + "t_expt1 = np.array(valPoints[\"t\"])\n", + "t_expt1 = t_expt1[:,np.newaxis] # Converting to a column vector\n", + "\n", + "gammadot1 = np.array(valPoints[\"shear rate\"])\n", + "gammadot1 = gammadot1[:,np.newaxis]\n", + "\n", + "shear_stress1 = np.array(valPoints[\"shear stress\"])\n", + "shear_stress1 = shear_stress1[:,np.newaxis] # experiment value\n", + "\n", + "\n", + "# Non-dimensionalizing #\n", + "t_non_dim = (t_expt1-time_min)/(del_time) #(t_expt1-0.5)/(600.0-0.5)\n", + "gammadot_non_dim = (gammadot1-shear_min)/(del_shear_rate) # (gammadot1-0.1)/(20.0-0.1) \n", + "\n", + "# using model to predict shear stress at a new shear rate #\n", + "test_data_set = np.hstack((gammadot_non_dim,t_non_dim))\n", + "#print(test_data_set)\n", + "\n", + "test_shear_stress = model.predict(test_data_set,operator=lambda x,y: y[:,0:1]) # Predicting shear stress\n", + "test_struct_param = model.predict(test_data_set,operator=lambda x,y: y[:,1:2]) # Predicting strcuture parameter\n", + "\n", + "# making non-dimensional to dimensional\n", + "test_shear_stress = test_shear_stress*(del_stress) + stress_min" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f4369833", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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1WZfCc8/lOiwRyXNKCnlql4X0OhVSutfNMHw4zJ+f28BEJK8pKeShlAvpbb2E6p4DYeRIWLgwtwGKSN5SUshDKYep1hmlg/4EvXsHW3wuWpSb4EQkr0VKCmZ2sJn9p5lNMbNJZjbQzKJeW2BmFWY2L3z9EzNbaWbLzOxRM9svPN7LzLaYWWX4mNni36qNS7eQ3sK/782QbhVUH3IynH8+LF6cmwBFJG81+sVuZkPN7EngceA8oDtwPPBd4BUzm25m3Zp4j68BryW9fho4wd1PAlYD05LOveHuxeHjymb+Lu1GumGqgwfDopc6U3r6k3DwwXDuuVBenutwRSSPNPXX/vnAFe7+WXef7O7fdfdvufto4GSgAjg73cVm1gMYAdydOObuT7l7TfhyMdBjj34DAXb2M9TVQdnD+1I9dwEccACcfXaQRUREImg0Kbj7t939nTTnatz99+7+20ZucTtwHVCX5vwk4Imk173DpqaFZnZGqgvMbLKZlZtZ+YYNGxoLv13ZZTRSLZSW9YBnn4WuXYPE8MoruQ1QRPJC5I5mMxthZteZ2fcTjybKjwTec/clac7fANQA94eHqoCe7t4PuBZ4IFXTlLvf6e4l7l5SVFQUNfw2bffRSMHrpR/1Ysihq6judDiceSasWJHbQEWk1YvaWTwTGAt8hWC5zjHAEU1cdhow2szWAnOBYWY2O7zfBGAkMN7dHcDdt7n7xvD5EuAN4Jjm/kLtUeptPWH8eFi0pAulw56FggIYNgxWr85NkCKSF6LWFE519y8C/3L36cAg4PDGLnD3ae7ew917AeOA+e5+qZkNB64HRrt7/ebDZlZkZgXh8z7A0cCbzf6N2qF0o5FWrAj7GB7dj+oHFwYvzjoL3knZIigiEjkpbA1/fmJmnwZ2AL1b+J6/BLoCTzcYejoYWGZmS4FHgCvd/YMWvke7kmo00lVXQadgm+egj+HBY6i6fz5D1s+heshYbfYsIil1jFjuj+F8gp8ALwMO3BX1Tdx9AbAgfH5UmjK/BRrrtJaI0vUxfPzxCSyqc0rXXcaMc86BBQuCEUoiIqEmawpm1o9gnkH38Iv7COAz7t5oR7PkTsp9nmtg9myCfZ47XE71yg/hvPNg06acxCgirVNTk9e+DzwI/BfwuJldEXYIf5SV6KRFUu7zvCNoRgKopYDSMxfAkiVwwQWwZUu2QxSRVqqpmsJYoNjdLwE+C0yOPyTZU+n3eQ5s3w6zFvRhUO8qqp99DcaMCbKGiLR7TSWFrYkRQuFwUS2gl4dSNSdt3w6L1xRROuRpePxx+MIXdlYlRKTdaqqj+Ugzeyx8bg1eEy53Ia1cquakRJIoe+kEvve9GRxaOgW6dYNf/xrMsh+kiLQKTSWFCxq8/mlcgUh8Gi59dPXVcM89QaKorYXSjVcz44b18MMfwkEHwf/8T24CFZGcazQpuLt2a2lj0g1X/d4bpRz6/vvwv/8LRUXwjW/kNlARyYmmRh/90cxGmVmnFOf6mNkPzGxSfOFJpqVbEqP0ZoMZM+Cii+Daa+E3v8lNgCKSU001H11BsDjd7Wb2AbABKAR6EaxN9Et3/0OsEUpGpVsS44UXCNZHmj0bPvgALrssmNg2YkRO4hSR3LBwPbqmC5r1IthkZwuwOnndolwpKSnxcm0ik3FVr29mXP9VPFhzEYf+ZTacdlquQxKRDDKzJe5ekupc5CGm7r7W3f/m7pWtISFIfEpv25dFn/SndK+bYeRI7cUg0o5o3oEAQQf0kCGwdGliBzejbNslVBf2Crb1fOutXIcoIlmgpCBA0AG9aFGwB0P9Dm51HSgd8hfYuhXOOQfefTe3QYpI7JqdFMxsfzM7KY5gJDeS93devrzBcNXHDmTp7c8y5M1ZVJ91KXykZa9E2rKoO68tMLNuZnYAsBQoM7OfxRuaZEuqYaoJtbUw/scns8hPp3T5hcECelu3pi4sInkvak3hU+7+b+BCoMzdBwBnxReWZEvDyWwN1e/g5kZZpyuoXrgS/vu/tU6SSBsVNSl0NLPuwMXAvBjjkSxLVUvo3DlYCmO3HdzoSOlpT8Kjj+4sICJtStSk8APgSWCNu/893EP59fjCkmxpbDJbyiUxXj6Z6q/+D9x5J0yfnv2ARSRWkbbjdPeHgYeTXr9JsPGO5LmGi+Ulu/rqNEti7JjKjElrgqRwyCFBdUJE2oSoHc0/DjuaO5nZM2b2vpldGvHaAjOrMLN54esDzOxpM3s9/Ll/UtlpZrbGzFaZ2bkt+5UkU9LWIv5mwRLbo0bBlCnwyCO5CVBEMi5q89E5YUfzSGAdcAzw7YjXfo1gj+eEqcAz7n408Ez4GjM7HhgH9AWGA3eYWUHE95AYNNzBLbGLW7duUP1+R5g7F049NZjcsGBBrsMVkQyImhQSq6SeD8xx9w+iXGRmPYARwN1Jhy8A7guf3wd8Pun43HAP6LeANcDAiPFJliQmuZWWAnvvDY89BkcfHQxVrazMdXgisoeiJoU/mtlKoAR4xsyKgCiD1W8HrgOSW6YPcfcqgPDnweHxw4B/JJVbFx7bhZlNNrNyMyvfsGFDxPAlE5InuZWVQXU1wUqqf/4zfOpTcN558OabuQ5TRPZApKTg7lOBQUCJu+8APmH3Xdl2YWYjgffcfUnEWFLtAbnbmEd3v9PdS9y9pKioKOKtJROSh6/W1sLUqcF6SdUde8CTTwYdDueeC++9l9tARaTFonY07w1MAX4VHvo0Qa2hMacBo81sLTAXGGZms4F3wzkPhD8T3yDrgMOTru8BrI8Sn8Qv1fDU2bPh+eeDZFG133EM6fkW1etq4PzzYdOm3AYsIi0StfmoDNgOnBq+Xgfc3NgF7j7N3Xu4ey+CDuT57n4p8BgwISw2AUhs0vMYMM7M9jKz3sDRwEtRfxGJV7od29yDZDFtGixa1o3SM58N+hYuvDD9NGkRabWiJoUj3f3HwA4Ad99C6uaeKG4Bzjaz14Gzw9e4+3LgIWAF8GdgirtrLYVWItXw1ISamqDWUFcHZfN7Uf2zB+Avf4EJE9IvqiQirVKkyWvAdjPrQtjGb2ZHAtuivom7LwAWhM83AmemKfdD4IdR7yvZkzzJraoK+vTZuS7ejh07z9XWQumqi5nx47fhuuvg4IPh9tvBWvo3hIhkU9SkcCPBX++Hm9n9BP0FE+MKSlq3xlZV3b49aE763hvf4tDqavjZz6B796BXWkRavSaTgpl1APYnWCH1FIJmo6+5+/sxxyatVGNNSRA0J/UfYLxc/hMOfffdoMPhkEPgssuyF6SItEiTfQruXgdc4+4b3f1xd5+nhNC+Jc90Li7e/fyOHUETU+kPO8CsWcEw1SuugD/+MeuxikjzRO1oftrMvmVmh4drFx0Qbrgj7VzDpTDWr4fCwuBcWRlUf9A5WBupf3+4+OJg+VURabWiJoVJBPMUngOWhI/yuIKS/JVygtuIfame9Sfo2RNGjgz2/BSRVsk8wkYpZlbo7lubOpZtJSUlXl6u3NRaNByVBFBQECSJq66CGd9eGyygV1AQ1BgOPzztvUQkPma2xN1TTkCOWlNIVedXO4DsoqkJbtWFvYJ1kjZtCvoZNm7MSZwikl6jScHMDjWzAUAXM+tnZv3Dx+eAvbMRoOSPxkYl1TclfeUkqsueCBbOGzkSPv44u0GKSKOaGpJ6LsF8hB7AreycxbwJ+E58YUk+amyCW2KtpLo6KD1hEDPmzoX/+i8YOzbY8zmxEbSI5FSjNQV3v8/dhwIT3X2Yuw8NH6Pd/XdZilHyUJNNSad8Hn71K3j88WC4aoS+LRGJX9Q+hR7hdpxmZneb2ctmdk6skUlei9SUdP9kqr99K9x3XzDBTURyLvKQ1HA7znMINsW5jHAhO5FUkucvJM9dgAbLbm/+Blx9NfzoR3DbbbkLWESA6Ekh0ZdwPlDm7ktp+Sqp0s401pQ0q8wYtOSXVI/8Elx7Ldx/f26CFBEgelJYYmZPESSFJ82sK7tusSmSVmNNSdu3w+IXjdLDfgVDh8LEifDUU1mNT0R2ipoULgemAp9190+AzgRNSCJNaqwpKVGDmHVfR6pn/h5OOCHYoOfvf89JrCLtXdQ9muvc/WV3/zB8vdHdl8UambRJ6Zbd3r4dpv5PN4YUvkj1AccHW3quXp39AEXauag1BZGMSNeUVFcXdj6/2JnSwU8Fm/Kcc05QtRCRrFFSkKxKNCVddRV07rzrufp5DL/bj+rfPB0sg3HeefDhhzmJVaQ9ajIpmFkHM3s1G8FI+9HkPIY5JzOk9ztUr/gALrgAtmzJboAi7VTUTXaWmlnP5tzYzArN7CUzW2pmy81senj8QTOrDB9rzawyPN7LzLYknZvZkl9I8kOkeQyv7s/UUxcy5LkfUD3qisa3exORjIi6R3N3YLmZvQTUr2Dm7qMbuWYbMMzdN5tZJ2CRmT3h7mMTBczsVuCjpGvecPfiyNFLm5BuHgPA7L/2oc56U/rMq3z3wimM+/evefChDhx6aPbjFGkPoiaF6c29sQcbNWwOX3YKH/UL3JiZARcDw5p7b2lbmmpKAqOs02Q+fnw2i4DSHzgz7tDcSZE4RNpkp8U3Nysg2KXtKGCGu1+fdG4w8LPERg9m1gtYDqwG/g18192fT3HPycBkgJ49ew54++23Y4tfsi/VRj0QLKJaV1NHrXegS8F23vxHJw7trsQg0hJ7vMmOmZ1iZn83s81mtt3Mas3s301d5+61YXNQD2CgmZ2QdPoSYE7S6yqgp7v3A64FHjCzbinueae7l7h7SVFRUZTwJY+km8ewYwfUepAEamvrKB2xWCurisQg6pDUXxJ8ib8OdAG+FB6LJJz0tgAYDmBmHYELgQeTymxz943h8yXAG8AxUd9D2obGmpISy21tp5CyipOpnvbzrMUl0l5Enqfg7muAgvCv/zLgc42VN7MiM9svfN4FOAtYGZ4+C1jp7usalC8In/cBjgbejP6rSFuQGJVUXNx4ua0UMu1Hn4KfKzGIZFLUpPCJmXUGKs3sx2b2DWCfJq7pDjxrZsuAvwNPu/u88Nw4dm06AhgMLDOzpcAjwJXu/kHE+KSNSR6ymipBOB34Y+cLqfr6LQw5porq6qyHKNImRepoNrMjgHcJFsL7BvAp4I6w9pAzJSUlXl5enssQJMuSO6K7dHEu3v8v/Gb9MK4cupoZ84/LdXgieWGPO5rd/W2CBt3u7j7d3a/NdUKQ9im5I7qmxpj97lnUUcCsZ49g0FHvqcYgsoeijj4aBVQCfw5fF5vZYzHGJbKbqqpgf+dER/SOHVBbm+h83ovFbxRROn4lVVUwZAhKECItELVP4SZgIPAhgLtXAr3iCEgknXTDVQHqKACMWfOPYNqYNSxaFJQXkeaJmhRq3P2jpouJxKfx4aqBbXRm9l97UVcHs2bBoEGqMYg0R9Sk8KqZ/TdQYGZHm9kvgBdijEtkN02NSAJwCqilAIDt2+pYvFg1BpHmiJoUvgL0JVjkbg7BMhRfjykmkSY1ti9DYpJbnQcf73vuUY1BJKqoo48+cfcb3P2z4RITN7j71qavFIlXpCalba4ag0hEUUcfHWNmd5rZU2Y2P/GIOziRpkRpUkrUHFRjEGla1Oajh4EK4LvAt5MeIq1G401Ku9YYNGxVJLWo+ynUuPuvYo1EJEPSNykFNYZZs5yPP7b6YaszZmQ1PJFWrdGagpkdYGYHAH80s6vNrHviWHhcpNVpssaw1Zk92zVsVSSFpmoKSwh2S0vsZpLcZORAnziCEsmEdDUGpwO1tcGaX9u3U9+kpBqDSBM1BXfv7e59wp8NH0oI0qpFGrYazpBWJ7RIoKnmo8+a2aFJr79oZn8ws/9T85Hki2jDVoMaw9Sp6oCW9q2p0Ue/BrZD/Z7KtwD/D/gIuDPe0EQyI9qw1cDs2fD885rTIO1XU0mhIGmjm7HAne7+W3f/HnBUvKGJZF5TndC1tcF5dUBLe9VkUgj3UwY4E0iesBZ1OKtIq9NUk1JyB7RIe9JUUpgDLDSzPwBbgOcBzOwogiYkkbzUVI1BHdDSXjU1+uiHwDeBe4HTfefenR0IFskTyWtN1RjUAS3tTZPLXLj7Ynd/1N0/Tjq22t1fbuw6Mys0s5fMbKmZLTez6eHxm8zsn2ZWGT7OT7pmmpmtMbNVZnbunvxiIlEkagxRO6CVHKSti7r2UUtsA4a5+8lAMTDczE4Jz93m7sXh408AZnY8MI5gie7hwB1mVhBjfCL1dm1O8t3OJzqgNTpJ2rrYkoIHNocvO4WP3f+37XQBMNfdt7n7W8Aagi1ARbImaE6ytOcTyaGsDJYuVa1B2p44awqYWYGZVQLvAU+7+4vhqWvMbJmZzTKz/cNjhwH/SLp8XXis4T0nm1m5mZVv2LAhzvClHWqqAzqhthbGj4dFi9SkJG1LrEnB3WvdvRjoAQw0sxOAXwFHEjQpVQG3hsVT/Xm2W83C3e8MN/opKSoqiiVukShDVpcvD0YpzZ4Nzz0H/fsrMUj+izUpJLj7h8ACYLi7vxsmizrgLnY2Ea0DDk+6rAewPhvxiTQUtQMagloDBHs0fPWrqjVIfostKZhZkZntFz7vApwFrDSz7knF/hN4NXz+GDDOzPYys97A0cBLccUnEkVzkgPAww+rI1ryW5w1he7As2a2DPg7QZ/CPODHZvZKeHwo8A0Ad18OPASsAP4MTHH32hjjE4ksef2kqy7aQGe2pS2rZTIkn9nO+Wj5p6SkxMvLy3MdhrQz/fpBZWXjZTp0CPobJkyAt96CBx+EQw9t/BqRbDGzJe5ekupcVvoURNqS+ialk9P/QZVYJkOT3iTfKCmItFBFpTXZ19Bw0puSg7R2Sgoie6C+1nBi491fSg6SL5QURDKgYllB0Al97puNdkIrOUhrpz0RRDLob+/2oYmdP4Gdcxtmzw76H6ZOVYe0tA6qKYhkUJRO6GTJNQfNipbWQElBJAYVlRYkh75R6g2aFS2th5KCSIwqXu0cJIdeH0a+JjErWv0NkgtKCiJZUPHWfvgH/6L4gHcilVdntOSKkoJItuy/PxUbe+IvvkTx3quaLJ5qpNIpp2j5DImXkoJItg0cSMWmo/Gyeynu+EqTxZOTw4svBntGa8E9iYuSgkgudOgAEydSsfEIiov+GemS2qT5cffcE9QYtPubZJqSgkgudetGxXuH4WveoHj/tZEv27YtqDFo9zfJNCUFkdbgyCOp+KAXXlFJcbc3Il+m3d8k05QURFqT4mIqPjoSX/gcxXuvjnyZ5jlIpigpiLRGgwdTsflo/C/PULzP6826NHmeg0YrSXMpKYi0VmZw5plBcnh+ET78PK7ijkYX3EtoOFop0eegjmlpinZeE8kj/Y79mMrV+zT7uoKCoO/h+ONhxYpg0b2XX9bie+2Vdl4TaSMqVu0T7BW99m38m9+iuMOySNcl5josXx78VN+DpBNbTcHMCoHngL0Iluh+xN1vNLOfAKOA7cAbwGXu/qGZ9QJeAxJTPRe7+5WNvYdqCtLubdpEv+O2UPnPg1t0uRl88YuwcmXw/NFHVXtoDxqrKcSZFAzYx903m1knYBHwNaAbMN/da8zsRwDufn2YFOa5+wlR30NJQSRUV0e/Yz6m8o2uzb60oGDn6KUJE7SvQ3uQk+YjD2wOX3YKH+7uT7l7TXh8MdAjrhhE2o0OHahY0zVYkfWEHc26NHmmtNZZklj7FMyswMwqgfeAp939xQZFJgFPJL3ubWYVZrbQzM5Ic8/JZlZuZuUbNmyIJ3CRPFbxSqeg36G2juIjNzXr2lTrLClBtC9ZGX1kZvsBjwJfcfdXw2M3ACXAhe7uZrYXsK+7bzSzAcDvgb7u/u9091XzkUhEmzbR74QdVL5zQLMvVfNS25Pz0Ufu/iGwABgeBjQBGAmM9zArufs2d98YPl9C0Al9TDbiE2nzunal4u0DmrUbXELD5qXEchqJOQ+a+9C2xJYUzKworCFgZl2As4CVZjYcuB4Y7e6fNChfED7vAxwNvBlXfCLtVf1ucMXNvzZ5OY2xY4PF+MaPD/ohtO5S29Axxnt3B+4Lv+g7AA+5+zwzW0MwTPXpYIBS/dDTwcAPzKwGqAWudPcPYoxPpF2rqNj5vF8/qKxs3vWrwsHjy5cHPxNzH955R8Nb85lmNItIvZYkh3QmTNg5/2HmzCBhqC+idch5n4KI5IeKimD0UUubl5Ilj2BKbmJSH0TrpqQgIintaYJI7qBOXl5j7FgliNZMSUFEmlRRsec1h4RVq5QgWjMlBRGJJLnmsGdNTDv7MRtLEJowlxtxjj4SkTYuMYKpeR3UlvTc618nRjMlEkTi9dSpWrAvm1RTEJE91vL+h4YJIpBICJB+yQ01N8VDSUFEMqphM1P0JJE6QdTW7nze2IgmNTdlhpKCiMSqZbUIS/k8OUEsX+679Ec0rE0MGKAaRUto8pqI5MSeT5Tb2R+R/Dx5AT+Avn13bkH6xBPw5S+rf6KxyWvqaBaRnNjTZTZ2rU3sFNQmdp5bvjx43VgH9syZQbLYsQM6d27nCUM1BRFpjTK35EaaGkUHp7YOwOjbd+caTrD7Eh1trXaRk+04s0FJQaR9iDtB7Po8fcLo3n3XJqh8TRha+0hE8lrLRzQ1lLoDu6FEQoBE81OgqsoZO+pjXnzRWbzYGT9+Zwd3w1FQ+ToiSklBRPJO5mZXJzRMEOkShrHqH3uH5233hPG5d3cmjFH/rn/ev7iWpeU7dhsV1RrnXKj5SETarEwuBb5T+manxpqnjmUlqzi2/ljffd5i+ce9g+f7r2fFv7pTtPfH9DloEzO/8Fe+PPdz7KgroHMnePTGSg49xKFTp6AnvFMnOOggOOKIFv0G6lMQEQnFkyhSaSxhNHydeB58H/flVZbTt/78BO5lJZ/BgJl8mS/za+zAA3n01WNa1I+hpCAiEkH2EkZT0nV8Q9+em1j+TlcArr7amDGj+XdXR7OISASZ76toqV37NGrrjPp+jHe61T+fNSvz/RBKCiIijUiVKHKbMHbavh1KSzN7z9iSgpkVmtlLZrbUzJab2fTw+AFm9rSZvR7+3D/pmmlmtsbMVpnZuXHFJiKSCblOGHV1ZLy2EGdNYRswzN1PBoqB4WZ2CjAVeMbdjwaeCV9jZscD44C+wHDgDjMriDE+EZHYNJYwMpk8Ml1biC0peGBz+LJT+HDgAuC+8Ph9wOfD5xcAc919m7u/BawBBsYVn4hIa7CntY26OnjhhczFE+uCeOFf+kuAo4AZ7v6imR3i7lUA7l5lZgeHxQ8DFiddvi481vCek4HJAD179owzfBGRnEpeNDBbYu1odvdady8GegADzeyERoqnmkK423hZd7/T3UvcvaSoqChDkYqICGRp9JG7fwgsIOgreNfMugOEP98Li60DDk+6rAewPhvxiYhIIM7RR0Vmtl/4vAtwFrASeAyYEBabAPwhfP4YMM7M9jKz3sDRwEtxxSciIruLs0+hO3Bf2K/QAXjI3eeZ2d+Ah8zscuAdYAyAuy83s4eAFUANMMXda9PcW0REYqBlLkRE2pk2u/aRmW0A3gY+BXyUpthBwPtZCyqzGvu9Wvt77cn9mnttc8pHKdtUGX3eWt976fPWPEe4e+qROu6e9w/gzkbOlec6vjh+r9b+Xntyv+Ze25zyUco2VUaft9b3Xvq8Ze7RVtY++mOuA4hJNn+vTL/Xntyvudc2p3yUsk2V0eet9b2XPm8ZktfNR1GYWbmnaTsTyTR93iSb4vi8tZWaQmPuzHUA0q7o8ybZlPHPW5uvKYiISHTtoaYgIiIRKSmIiEg9JQUREanXrpOCmfUxs3vM7JFcxyJtk5ntY2b3mdldZjY+1/FI25aJ77S8TQpmNsvM3jOzVxscHx5u57nGzKY2dg93f9PdL483UmlrmvnZuxB4xN2vAEZnPVjJe835vGXiOy1vkwJwL8FS3PXCxfdmAOcBxwOXmNnxZnaimc1r8Dh491uKRHIvET97BEvA/yMspgUepSXuJfrnbY/FuvNanNz9OTPr1eDwQGCNu78JYGZzgQvc/X+BkVkOUdqo5nz2CPYJ6QFUkt9/hEmONPPztmJP36+tfUgPY+dfZZBmS88EMzvQzGYC/cxsWtzBSZuW7rP3O+C/zOxXtN3lMST7Un7eMvGdlrc1hTQibelZf8J9I3BlfOFIO5Lys+fuHwOXZTsYafPSfd72+DutrdUUtKWn5Io+e5JNsX3e2lpS+DtwtJn1NrPOwDiCbT5F4qbPnmRTbJ+3vE0KZjYH+BtwrJmtM7PL3b0GuAZ4EniNYAvQ5bmMU9oeffYkm7L9edOCeCIiUi9vawoiIpJ5SgoiIlJPSUFEROopKYiISD0lBRERqaekICIi9ZQURESknpKCCPWLI1aGj2oz+2f4fLOZ3RHTe37dzL7YyPmRZjY9jvcWSUeT10QaMLObgM3u/tMY36Mj8DLQP5ydmqqMhWVOc/dP4opFJJlqCiKNMLPPmdm88PlN4daaT5nZWjO70Mx+bGavmNmfzaxTWG6AmS00syVm9qSZdU9x62HAy4mEYGZfNbMVZrYsXBsfD/5iW4D2ApEsUlIQaZ4jgREEG5rMBp519xOBLcCIMDH8ArjI3QcAs4AfprjPacCSpNdTgX7ufhK7Ln1cDpyR8d9CJI22tp+CSNyecPcdZvYKUAD8OTz+CtALOBY4AXg6aP2hAKhKcZ/uBAuZJSwD7jez3wO/Tzr+HvDpzIUv0jglBZHm2Qbg7nVmtsN3dsrVEfx/MmC5uw9q4j5bgMKk1yOAwcBo4Htm1jdsWioMy4pkhZqPRDJrFVBkZoMAzKyTmfVNUe414KiwTAfgcHd/FrgO2A/YNyx3DPBq3EGLJCgpiGSQu28HLgJ+ZGZLgUrg1BRFnyCoGUDQxDQ7bJKqAG5z9w/Dc0OBx+OMWSSZhqSK5IiZPQpc5+6vpzl/CPCAu5+Z3cikPVNSEMkRMzsWOMTdn0tz/rPADnevzGpg0q4pKYiISD31KYiISD0lBRERqaekICIi9ZQURESknpKCiIjU+/8SlzB7WUU1+wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting structure parameter with time #\n", + "plt.figure()\n", + "plt.plot(t_expt1,test_struct_param, 'ok', label='PINN-Structure-Parameter')\n", + "plt.title('')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Structure parameter')\n", + "plt.show()\n", + "\n", + "# Plotting shear stress with time #\n", + "plt.figure()\n", + "plt.plot(t_expt1,test_shear_stress, '-r', label='PINN')\n", + "plt.plot(t_expt1,shear_stress1, '^b', label='Experiment')\n", + "plt.legend()\n", + "plt.xscale('log')\n", + "#plt.title('8/s - prediction')\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Shear stress (Pa)')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "0605e0af", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restoring model from model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1499000.ckpt ...\n", + "\n", + "INFO:tensorflow:Restoring parameters from model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1499000.ckpt\n" + ] + } + ], + "source": [ + "# Restore the saved model with the smallest training loss - Run initial steps until compilation\n", + "# enter the correct model number\n", + "model.restore(f\"model-DOWTC5622-TEVP-final3-7s/model-DOWTC5622-TEVP-final3-1499000.ckpt\", verbose=1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "79e289e1", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Plotting training and test data for manuscript #\n", + "\n", + "plt.rcParams.update({'font.size': 16})\n", + "\n", + "for itrnew in range(1,10):\n", + " if itrnew == 6: # skipping 7/s data\n", + " continue\n", + " valPoints = pd.read_excel('DOWTC5622-09-02-22.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=itrnew,skiprows=range(148,296))\n", + " \n", + " t_expt1 = np.array(valPoints[\"t\"])\n", + " t_expt1 = t_expt1[:,np.newaxis] # Converting to a column vector\n", + "\n", + " gammadot1 = np.array(valPoints[\"shear rate\"])\n", + " gammadot1 = gammadot1[:,np.newaxis]\n", + "\n", + " shear_stress1 = np.array(valPoints[\"shear stress\"])\n", + " shear_stress1 = shear_stress1[:,np.newaxis] # experiment value\n", + "\n", + "\n", + " t_non_dim = (t_expt1-time_min)/(del_time) #(t_expt1-0.5)/(600.0-0.5)\n", + " gammadot_non_dim = (gammadot1-shear_min)/(del_shear_rate) # (gammadot1-0.1)/(20.0-0.1) \n", + "\n", + " # using model to predict shear stress at a new shear rate #\n", + "\n", + " test_data_set = np.hstack((gammadot_non_dim,t_non_dim))\n", + "\n", + " test_shear_stress = model.predict(test_data_set,operator=lambda x,y: y[:,0:1]) # only predicting the shear stress and not struct param\n", + " #test_struct_param = model.predict(test_data_set,operator=lambda x,y: y[:,1:2])\n", + " # making non-dimensional to dimensional\n", + " test_shear_stress = test_shear_stress*(del_stress) + stress_min\n", + " \n", + " \n", + " #plt.figure()\n", + " if itrnew == 1:\n", + " plt.plot(t_expt1,test_shear_stress, '-r', label='PINN')\n", + " plt.plot(t_expt1,shear_stress1, '^b', label='Experiment: training data')\n", + " elif itrnew == 6:\n", + " plt.plot(t_expt1,test_shear_stress, '-k', label='PINN: prediction '+str(itrnew+1)+'/s')\n", + " plt.plot(t_expt1,shear_stress1, '^g', label='Experiment: test data '+str(itrnew+1)+'/s')\n", + " else:\n", + " plt.plot(t_expt1,test_shear_stress, '-r')\n", + " plt.plot(t_expt1,shear_stress1, '^b')\n", + "\n", + "plt.xlim([0.1, 10])\n", + "plt.legend(loc='upper right')\n", + "plt.xscale('log')\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "plt.savefig('Shear_stress_trainingdata_DOW5622_latest.eps',format='eps',bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "valPoints = pd.read_excel('DOWTC5622-09-02-22.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=6,skiprows=range(148,296))\n", + " \n", + "t_expt1 = np.array(valPoints[\"t\"])\n", + "t_expt1 = t_expt1[:,np.newaxis] # Converting to a column vector\n", + "\n", + "gammadot1 = np.array(valPoints[\"shear rate\"])\n", + "gammadot1 = gammadot1[:,np.newaxis]\n", + "\n", + "shear_stress1 = np.array(valPoints[\"shear stress\"])\n", + "shear_stress1 = shear_stress1[:,np.newaxis] # experiment value\n", + "\n", + "\n", + "t_non_dim = (t_expt1-time_min)/(del_time) #(t_expt1-0.5)/(600.0-0.5)\n", + "gammadot_non_dim = (gammadot1-shear_min)/(del_shear_rate) # (gammadot1-0.1)/(20.0-0.1) \n", + "\n", + " # using model to predict shear stress at a new shear rate #\n", + "\n", + "test_data_set = np.hstack((gammadot_non_dim,t_non_dim))\n", + "test_shear_stress = model.predict(test_data_set,operator=lambda x,y: y[:,0:1]) # only predicting the shear stress and not struct param\n", + "test_shear_stress = test_shear_stress*(del_stress) + stress_min\n", + "\n", + "plt.plot(t_expt1,test_shear_stress, '-k', label='PINN')\n", + "plt.plot(t_expt1,shear_stress1, '^g', label='Experiment: test data')\n", + " \n", + "# plt.figure()\n", + "#plt.xlim([0.1, 10])\n", + "plt.legend(loc='upper right')\n", + "plt.xscale('log')\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "plt.savefig('Shear_stress_testdata_DOW5622_7s_latest.eps',format='eps',bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "# Plotting lambda vs t \n", + "\n", + "test_lambda = model.predict(test_data_set,operator=lambda x,y: y[:,1:2]) # only predicting the shear stress and not struct param\n", + "\n", + "plt.plot(t_expt1,test_lambda, '-k')\n", + " \n", + "# plt.figure()\n", + "#plt.xlim([0.1, 10])\n", + "#plt.legend(loc='upper right')\n", + "plt.xscale('log')\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\lambda$',fontsize=16)\n", + "plt.savefig('Lambda_testdata_DOW5622_7s_latest.eps',format='eps',bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "966c44ab", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "### Solving direct problem based on odeint for validation purposes ###\n", + "plt.rcParams.update({'font.size': 16})\n", + "\n", + "from scipy.integrate import odeint\n", + "\n", + "# Enter correct file name \n", + "valPoints1 = pd.read_excel('DOW5622_lowShearRates_101222.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=5)\n", + "t_expt2 = np.array(valPoints1[\"t\"])\n", + "t_expt2 = t_expt2[:,np.newaxis] # Converting to a column vector\n", + "\n", + "gammadot2 = np.array(valPoints1[\"shear rate\"])\n", + "gammadot2 = gammadot2[:,np.newaxis]\n", + "\n", + "shear_stress2 = np.array(valPoints1[\"shear stress\"])\n", + "shear_stress2 = shear_stress2[:,np.newaxis] # experiment value\n", + "\n", + "\n", + "def dydt(y,t,G,eta_s,eta_p,k_plus,k_minus,yield_stress):\n", + " \n", + " shear_rate_ode = (1-np.heaviside(t-158.25,1))*1.0 + (np.heaviside(t-158.25,1)-np.heaviside(t-180.042,1))*0.1 \\\n", + " + (np.heaviside(t-180.073,1)-np.heaviside(t-338.292,1))*1.0\n", + " \n", + " y1, y2 = y\n", + " dydt = [(G/(eta_s+eta_p))*(-y1 + yield_stress*y2 + (eta_s + eta_p*y2)*shear_rate_ode), k_plus*(1-y2) - k_minus*y2*shear_rate_ode]\n", + " return dydt\n", + "\n", + "t = t_expt2.reshape(np.size(t_expt2),) #Non-dimensional time\n", + "y0 = [133.79,0.5] # setting IC for shear stress and structure parameter\n", + "\n", + "# Assign optimized model parameters from PINN\n", + "G_ode = 190.0 \n", + "eta_s_ode = 39.4 \n", + "eta_p_ode = 30.8 \n", + "k_plus_ode = 0.061 \n", + "k_minus_ode = 0.063\n", + "yield_stress_ode = 30.5 \n", + "\n", + "\n", + "stress_ode = odeint(dydt,y0,t,args=(G_ode,eta_s_ode,eta_p_ode,k_plus_ode,k_minus_ode,yield_stress_ode))\n", + "\n", + "plt.plot(t_expt2,stress_ode[:,0], 'or', label='Forward problem') \n", + "plt.plot(t_expt2,shear_stress2, '^b', label='Experiment')\n", + "plt.legend()\n", + "\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "plt.savefig('StepStrain_Val_DOW5622_latest.eps',format='eps',bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "60bab74e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling model...\n", + "Warning: For the backend tensorflow.compat.v1, `external_trainable_variables` is ignored, and all trainable ``tf.Variable`` objects are automatically collected.\n", + "'compile' took 0.507090 s\n", + "\n", + "Training model...\n", + "\n", + "1500091 [2.03e-06, 2.45e-06, 2.48e-04] [2.54e-06, 2.18e-06, 2.48e-04] [] \n", + "INFO:tensorflow:Optimization terminated with:\n", + " Message: CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH\n", + " Objective function value: 0.000252\n", + " Number of iterations: 1\n", + " Number of functions evaluations: 18\n", + "1500109 [2.03e-06, 2.45e-06, 2.48e-04] [2.54e-06, 2.18e-06, 2.48e-04] [] \n", + "\n", + "Best model at step 1500091:\n", + " train loss: 2.52e-04\n", + " test loss: 2.52e-04\n", + " test metric: []\n", + "\n", + "'train' took 1.818854 s\n", + "\n", + "Saving loss history to C:\\Users\\pnagrani\\loss.dat ...\n", + "Saving training data to C:\\Users\\pnagrani\\train.dat ...\n", + "Saving test data to C:\\Users\\pnagrani\\test.dat ...\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling model...\n", + "Warning: For the backend tensorflow.compat.v1, `external_trainable_variables` is ignored, and all trainable ``tf.Variable`` objects are automatically collected.\n", + "'compile' took 0.630434 s\n", + "\n", + "Training model...\n", + "\n", + "Step Train loss Test loss Test metric\n", + "1500109 [2.03e-06, 2.45e-06, 2.48e-05] [2.54e-06, 2.18e-06, 2.48e-05] [] \n", + "\n", + "Best model at step 1500109:\n", + " train loss: 2.93e-05\n", + " test loss: 2.95e-05\n", + " test metric: []\n", + "\n", + "'train' took 0.628264 s\n", + "\n" + ] + } + ], + "source": [ + "# If needed can perform further training using L-BFGS optimizer. We skipped as it did not improve the loss significantly #\n", + "\n", + "model.compile(\"L-BFGS-B\",\n", + " loss_weights=[1,1,1e1],\n", + " external_trainable_variables=[G, eta_s, k_plus, k_minus])\n", + "\n", + "model.train_step.optimizer_kwargs = {'options': {'maxfun': 1e5, 'ftol': 1e-20, 'gtol': 1e-20,'eps': 1e-20, 'iprint': -1, 'maxiter': 1e5}}\n", + "\n", + "variable = dde.callbacks.VariableValue([G, eta_s, k_plus, k_minus], period=10, filename=\"variables1-7s_DOWTC5622_LBFGSB.dat\")\n", + "losshistory, trainstate = model.train(callbacks = [variable])\n", + "dde.saveplot(losshistory, trainstate, issave=True, isplot=True)\n", + "\n", + "variable = dde.callbacks.VariableValue([G, eta_s, k_plus, k_minus], period=1, filename=\"dummy-4s.txt\")\n", + "model.compile(\"adam\", lr=0, external_trainable_variables=[G, eta_s, k_plus, k_minus])\n", + "losshistory2, train_state2 = model.train(epochs=0, callbacks=[variable], display_every=1)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "02dd3ef2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Generating plot of experimental data - Not a part of PINN process #\n", + "\n", + "plt.rcParams.update({'font.size': 16})\n", + "cmap = plt.colormaps['Blues']\n", + "\n", + "for itr in range(1,10): \n", + " \n", + " t_expt = []\n", + " gammadot = []\n", + " shear_stress = []\n", + " \n", + " startUpFlow = pd.read_excel('DOWTC5622-09-02-22.xlsx',header = None, names=['shear rate','t','shear stress'], sheet_name=itr, skiprows=range(148,296))\n", + " # Reading data from sheet number - itr\n", + " t_expt = np.array(startUpFlow[\"t\"])\n", + " #gammadot = np.array(startUpFlow[\"shear rate\"])\n", + " shear_stress = np.array(startUpFlow[\"shear stress\"])\n", + " \n", + " plt.plot(t_expt,shear_stress,color=cmap(itr*20), label='$\\dot\\gamma = $' +str(itr+1)+' $ s^{-1}$',marker='^',linestyle = 'None')\n", + " \n", + "plt.arrow(0.5,150,0.3,450, fc=\"k\",ec='k',width=0.01,length_includes_head='True',head_length=50,head_width=0.2)\n", + "plt.legend(loc='upper right',prop={'size': 11})\n", + "plt.xscale('log')\n", + "#plt.ylim([0, 800])\n", + "plt.xlabel(r'$t$ (s)',fontsize=16)\n", + "plt.ylabel(r'$\\sigma$ (Pa)',fontsize=16)\n", + "#plt.savefig('Shear_stress_experiment_DOW5622.eps',format='eps',bbox_inches='tight')\n", + "plt.show()\n", + "\n", + " \n", + " \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebe06525", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}