diff --git a/04d_TrainModels_ANN.py b/04d_TrainModels_ANN.py
new file mode 100644
index 0000000000000000000000000000000000000000..2a1339fc1e74e225d84e601e615aaafc0fdb97f3
--- /dev/null
+++ b/04d_TrainModels_ANN.py
@@ -0,0 +1,132 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+# from models.RegressionModels import RegressionModels
+from models.LinearRegression import LinearRegressionModel
+from models.GaussianProcessRegressor import GaussianProcessRegressionModel
+from models.RBFInterpolator import RadialBasisRegressionModel
+from models.ANN import ANN
+
+models = {
+    # 'linregMod': LinearRegressionModel(),
+    # 'gaussianProcessMod': GaussianProcessRegressionModel(),
+    'ANN': ANN(),
+}
+
+EPOCHS = 1_000
+BATCH_SIZE = 32
+LEARNING_RATE = 1e-2
+PLOT_LEARNING = True
+
+
+for i, model_name in enumerate(models):
+    print(f'Model: \t\t\t{model_name}')
+
+    model = models[model_name]
+
+    # # Use elbow point for the number of POD modes
+    # X_r_velocity, X_r_pressure = model.POD()
+    # model_velocity, model_pressure = model.train(X_r_velocity, X_r_pressure, lr=LEARNING_RATE, epochs=EPOCHS, batch_size=BATCH_SIZE, plot_learning=PLOT_LEARNING)
+
+    # model.save_model(model_velocity, model_pressure, model_velocity_file=f'{model_name}/velocity_elbow.pkl', model_pressure_file=f'{model_name}/pressure_elbow.pkl')
+
+    # Search for the best number of POD modes concerning the rmse (root mean square error)
+    # R = np.linspace(1, 11, 6, dtype=int)
+    R = np.linspace(21, 21, 1, dtype=int)
+
+    # best_model_velocity, best_model_pressure = model_velocity, model_pressure
+    # R_best = model.elbow_point(scaled=True)
+    # L2_velocity_best, L2_pressure_best = model.relative_norm(model_velocity, model_pressure, ord=2)
+
+    print(f'Finding the optimal number of nodes for model {model_name}')
+    print(f'Searching for best R in {R}')
+    L2_velocity_vec, L2_pressure_vec = [], []
+    Linf_velocity_vec, Linf_pressure_vec = [], []
+    L1_velocity_vec, L1_pressure_vec = [], []
+    for R_ in R:
+        print(f'Current R: {R_}')
+        X_r_velocity, X_r_pressure = model.POD(R_velocity=R_, R_pressure=R_)
+        model_velocity, model_pressure = model.train(X_r_velocity, X_r_pressure, lr=LEARNING_RATE, epochs=EPOCHS, batch_size=BATCH_SIZE, plot_learning=PLOT_LEARNING)
+
+        L2_velocity_, L2_pressure_ = model.relative_norm(model_velocity, model_pressure, ord=2)
+        L2_velocity_vec.append(L2_velocity_)
+        L2_pressure_vec.append(L2_pressure_)
+        Linf_velocity_, Linf_pressure_ = model.relative_norm(model_velocity, model_pressure, ord=np.inf)
+        Linf_velocity_vec.append(Linf_velocity_)
+        Linf_pressure_vec.append(Linf_pressure_)
+        L1_velocity_, L1_pressure_ = model.relative_norm(model_velocity, model_pressure, ord=1)
+        L1_velocity_vec.append(L1_velocity_)
+        L1_pressure_vec.append(L1_pressure_)
+
+        # ! Adapt if statements and R_best
+        # if L2_velocity_ < L2_velocity_best:
+        if True:
+            best_model_velocity = model_velocity
+            L2_velocity_best = L2_velocity_
+            L2_pressure_best = L2_pressure_
+            # R_best = (R_, R_best[1])
+            R_best = (R_, R_)
+        # if L2_pressure_ < L2_pressure_best:
+        if True:
+            best_model_pressure = model_pressure
+            L2_velocity_best = L2_velocity_
+            L2_pressure_best = L2_pressure_
+            # R_best = (R_best[0], R_)
+            R_best = (R_, R_)
+
+        model.save_model(model_velocity, model_pressure, model_velocity_file=f'{model_name}/velocity_{R_}.pkl', model_pressure_file=f'{model_name}/pressure_{R_}.pkl')
+    
+    # model.save_model(best_model_velocity, best_model_pressure, model_velocity_file=f'{model_name}/velocity_{R_best[0]}.pkl', model_pressure_file=f'{model_name}/pressure_{R_best[1]}.pkl')
+    model.save_model(best_model_velocity, best_model_pressure, model_velocity_file=f'{model_name}/velocity.pkl', model_pressure_file=f'{model_name}/pressure.pkl')
+
+    print(f'Optimal setup:')
+    print('---------------------------------')
+    print(f'Model: {model_name}')
+    print(f'\t \t\t Velocity \t Pressure')
+    print(f'$L_2$: \t\t\t {L2_velocity_best} \t {L2_pressure_best}')
+    print(f'Number of POD modes: \t {R_best[0]} \t {R_best[1]}')
+    print(f'Elbow point: \t\t {model.elbow_point(scaled=True)[0]} \t {model.elbow_point(scaled=True)[1]}')
+    print('---------------------------------')
+    print('\n \n')
+
+    np.savez(f'Results/{model_name}/OptimalSetup.npz', Elbow_points=np.array([model.elbow_point(scaled=True)[0], model.elbow_point(scaled=True)[1]]), R_best=R_best, L2_velocity_vec=L2_velocity_vec, L2_pressure_vec=L2_pressure_vec, R=R, Linf_velocity_vec=Linf_velocity_vec, Linf_pressure_vec=Linf_pressure_vec, L1_velocity_vec=L1_velocity_vec, L1_pressure_vec=L1_pressure_vec)
+
+    norm_names = ['L_2', 'L_infty', 'L_1']
+    norm_velocities = [L2_velocity_vec, Linf_velocity_vec, L1_velocity_vec]
+    norm_pressures = [L2_pressure_vec, Linf_pressure_vec, L1_pressure_vec]
+    for norm_name, norm_velocity, norm_pressure in zip(norm_names, norm_velocities, norm_pressures):
+
+        ylabel = f'${norm_name}$'
+        if norm_name == 'L_infty':
+            ylabel = '$L_\infty$'
+
+        fig, axs = plt.subplots(nrows=1, ncols=2)
+        fig.suptitle(f'{model_name} - Error over number of POD modes')
+
+        axs[0].plot(R, norm_velocity)
+        axs[0].set_title('u')
+        axs[0].set_xlabel('R')
+        axs[0].set_ylabel(f'${norm_name}$')
+        axs[1].plot(R, norm_pressure)
+        axs[1].set_title('p')
+        axs[1].set_xlabel('R')
+        axs[1].set_ylabel(f'${norm_name}$')
+
+        fig.tight_layout()
+        fig.savefig(f'Results/{model_name}/Error_POD_Nodes_{norm_name}.jpg')
+
+
+        fig, axs = plt.subplots(nrows=1, ncols=2)
+        fig.suptitle(f'{model_name} - Error over number of POD modes')
+
+        axs[0].semilogy(R, norm_velocity)
+        axs[0].set_title('u')
+        axs[0].set_xlabel('R')
+        axs[0].set_ylabel(f'${norm_name}$')
+        axs[1].semilogy(R, norm_pressure)
+        axs[1].set_title('p')
+        axs[1].set_xlabel('R')
+        axs[1].set_ylabel(f'${norm_name}$')
+
+        fig.tight_layout()
+        fig.savefig(f'Results/{model_name}/Error_POD_Nodes_semilogy_{norm_name}.jpg')
diff --git a/99_VisReconstructMethods.ipynb b/99_VisReconstructMethods.ipynb
index 5c159ae6d0fe540a05386e6d714112649c92f3d0..ad43610bbd1b1613e872bff7910638c16d287f3a 100644
--- a/99_VisReconstructMethods.ipynb
+++ b/99_VisReconstructMethods.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17,20 +17,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
     "from models.LinearRegression import LinearRegressionModel\n",
     "from models.GaussianProcessRegressor import GaussianProcessRegressionModel\n",
     "from models.RBFInterpolator import RadialBasisRegressionModel\n",
+    "from models.ANN import ANN\n",
     "\n",
     "from miscellaneous.DataPreparation import reconstruct_array, save_numpy_to_xml"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,21 +40,22 @@
     "\n",
     "path_to_executable = 'stokes_recreation/stokes_recreation'\n",
     "models = {\n",
-    "    'linregMod': LinearRegressionModel(),\n",
-    "    'gaussianProcessMod_DotWhite': GaussianProcessRegressionModel(),\n",
-    "    'gaussianProcessMod_RBF': GaussianProcessRegressionModel(),\n",
-    "    'gaussianProcessMod_Matern': GaussianProcessRegressionModel(),\n",
-    "    'gaussianProcessMod_RationalQuadratic': GaussianProcessRegressionModel(),\n",
-    "    'gaussianProcessMod_ExpSineSquared': GaussianProcessRegressionModel(),\n",
-    "    'radialBasisMod': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_Linear': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_thinplatespline': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_cubic': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_quintic': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_multiquadric': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_inversemultiquadric': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_inversequadratic': RadialBasisRegressionModel(),\n",
-    "    'radialBasisMod_gaussian': RadialBasisRegressionModel(),\n",
+    "    # 'linregMod': LinearRegressionModel(),\n",
+    "    # 'gaussianProcessMod_DotWhite': GaussianProcessRegressionModel(),\n",
+    "    # 'gaussianProcessMod_RBF': GaussianProcessRegressionModel(),\n",
+    "    # 'gaussianProcessMod_Matern': GaussianProcessRegressionModel(),\n",
+    "    # 'gaussianProcessMod_RationalQuadratic': GaussianProcessRegressionModel(),\n",
+    "    # 'gaussianProcessMod_ExpSineSquared': GaussianProcessRegressionModel(),\n",
+    "    # 'radialBasisMod': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_Linear': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_thinplatespline': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_cubic': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_quintic': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_multiquadric': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_inversemultiquadric': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_inversequadratic': RadialBasisRegressionModel(),\n",
+    "    # 'radialBasisMod_gaussian': RadialBasisRegressionModel(),\n",
+    "    'ANN': ANN(),\n",
     "}\n",
     "\n",
     "df = pd.read_excel(f'Data/test/parameter_input.xlsx').transpose()\n",
@@ -67,349 +69,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \t\t\tlinregMod\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tgaussianProcessMod_DotWhite\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tgaussianProcessMod_RBF\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tgaussianProcessMod_Matern\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tgaussianProcessMod_RationalQuadratic\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tgaussianProcessMod_ExpSineSquared\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_Linear\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_thinplatespline\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_cubic\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_quintic\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_multiquadric\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_inversemultiquadric\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_inversequadratic\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n",
-      "Model: \t\t\tradialBasisMod_gaussian\n",
-      "Singular value: \t1.0\n",
-      "Singular value: \t2.0\n",
-      "Singular value: \t3.0\n",
-      "Singular value: \t4.0\n",
-      "Singular value: \t5.0\n",
-      "Singular value: \t6.0\n",
-      "Singular value: \t7.0\n",
-      "Singular value: \t8.0\n",
-      "Singular value: \t9.0\n",
-      "Singular value: \t10.0\n",
-      "Singular value: \t11.0\n",
-      "Singular value: \t12.0\n",
-      "Singular value: \t13.0\n",
-      "Singular value: \t14.0\n",
-      "Singular value: \t15.0\n",
-      "Singular value: \t16.0\n",
-      "Singular value: \t17.0\n",
-      "Singular value: \t18.0\n",
-      "Singular value: \t19.0\n",
-      "Singular value: \t20.0\n",
-      "Singular value: \t21.0\n"
+      "Model: \t\t\tANN\n",
+      "Singular value: \t21\n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step \n",
+      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step \n"
      ]
     }
    ],
    "source": [
-    "SINGULAR_VALUES = np.linspace(1,21,21)\n",
-    "# SINGULAR_VALUES = [1,3]\n",
+    "# SINGULAR_VALUES = np.linspace(1,21,21)\n",
+    "SINGULAR_VALUES = [21]\n",
     "L1_rel_p_vec, L2_rel_p_vec, Linf_rel_p_vec = [], [], []\n",
     "L1_rel_v_vec, L2_rel_v_vec, Linf_rel_v_vec = [], [], []\n",
     "\n",
@@ -549,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -563,82 +239,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/v_/5q1gkdc53z34pdsfnpkx2t340000gn/T/ipykernel_45809/160479393.py:23: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
-      "  fig.show()\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 16 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "n_subfigs = len(models)\n",
-    "ncols = 2\n",
-    "nrows = n_subfigs // ncols + 1\n",
-    "fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 10))\n",
-    "\n",
-    "for i, model_name in enumerate(models):\n",
-    "    row = i // ncols\n",
-    "    col = i % ncols\n",
+    "# n_subfigs = len(models)\n",
+    "# ncols = 2\n",
+    "# nrows = n_subfigs // ncols + 1\n",
+    "# fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(10, 10))\n",
     "\n",
-    "# axs[0,0].plot(SINGULAR_VALUES, L1_rel_v_vec[0], label='L1')\n",
-    "    # axs[row, col].plot(SINGULAR_VALUES, L1_rel_p_vec[i], label='L1')\n",
-    "    axs[row, col].semilogy(SINGULAR_VALUES, L2_rel_p_vec[i], label='L2')\n",
-    "    # axs[row, col].plot(SINGULAR_VALUES, Linf_rel_p_vec[i], label='Linf')\n",
-    "    axs[row, col].set_title(f'{model_name}')\n",
-    "    axs[row, col].set_xlabel('Singular Value')\n",
-    "    axs[row, col].set_ylabel('$L_2$ (rel.)')\n",
-    "    # axs[row, col].legend()\n",
+    "# for i, model_name in enumerate(models):\n",
+    "#     row = i // ncols\n",
+    "#     col = i % ncols\n",
+    "#     axs[row, col].semilogy(SINGULAR_VALUES, L2_rel_p_vec[i], label='L2')\n",
+    "#     axs[row, col].set_title(f'{model_name}')\n",
+    "#     axs[row, col].set_xlabel('Singular Value')\n",
+    "#     axs[row, col].set_ylabel('$L_2$ (rel.)')\n",
     "\n",
-    "fig.suptitle('Errors - recreated - Pressure')\n",
-    "fig.tight_layout()\n",
-    "fig.savefig('Results/ConvergenceSVDRecreatedPressure.png')\n",
-    "fig.savefig('Results/ConvergenceSVDRecreatedPressure.pdf')\n",
-    "fig.show()"
+    "# fig.suptitle('Errors - recreated - Pressure')\n",
+    "# fig.tight_layout()\n",
+    "# fig.savefig('Results/ConvergenceSVDRecreatedPressure.png')\n",
+    "# fig.savefig('Results/ConvergenceSVDRecreatedPressure.pdf')\n",
+    "# fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# model_names = list(models.keys())\n",
+    "# plt.figure()\n",
+    "# plt.bar(model_names, L2_rel_p_vec[:,-1], color='b')\n",
+    "# plt.title('L2 relative errors (21 Singular values) - Pressure')\n",
+    "# plt.xticks(rotation=90)\n",
+    "# plt.ylim(1e-2, 1)\n",
+    "# plt.yscale('log')\n",
+    "# plt.tight_layout()\n",
+    "# plt.savefig('Results/ComparisonModelsRecreatedPressure.png')\n",
+    "# plt.savefig('Results/ComparisonModelsRecreatedPressure.pdf')\n",
+    "# plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
       "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
+       "array([[3.44496305]])"
       ]
      },
+     "execution_count": 12,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "model_names = list(models.keys())\n",
-    "plt.figure()\n",
-    "plt.bar(model_names, L2_rel_p_vec[:,-1], color='b')\n",
-    "plt.title('L2 relative errors (21 Singular values) - Pressure')\n",
-    "plt.xticks(rotation=90)\n",
-    "plt.ylim(1e-2, 1)\n",
-    "plt.yscale('log')\n",
-    "plt.tight_layout()\n",
-    "plt.savefig('Results/ComparisonModelsRecreatedPressure.png')\n",
-    "plt.savefig('Results/ComparisonModelsRecreatedPressure.pdf')\n",
-    "plt.show()"
+    "L2_rel_p_vec"
    ]
   },
   {
diff --git a/Results/ANN/Error_POD_Nodes_L_1.jpg b/Results/ANN/Error_POD_Nodes_L_1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..58944e8b987cb374ef9007e502400635a4ec4895
Binary files /dev/null and b/Results/ANN/Error_POD_Nodes_L_1.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_L_2.jpg b/Results/ANN/Error_POD_Nodes_L_2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bf9d929aa3704dcf46166f14448da5ec644fd7bc
Binary files /dev/null and b/Results/ANN/Error_POD_Nodes_L_2.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_L_infty.jpg b/Results/ANN/Error_POD_Nodes_L_infty.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f8bb304435838f444a003cf03e1900b4583633f4
Binary files /dev/null and b/Results/ANN/Error_POD_Nodes_L_infty.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_semilogy_L_1.jpg b/Results/ANN/Error_POD_Nodes_semilogy_L_1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..1526093175c06716b857d582073e55c04c0f1251
Binary files /dev/null and b/Results/ANN/Error_POD_Nodes_semilogy_L_1.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_semilogy_L_2.jpg b/Results/ANN/Error_POD_Nodes_semilogy_L_2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..f6c308e782149fb941027361278e2becd756c6de
Binary files /dev/null and b/Results/ANN/Error_POD_Nodes_semilogy_L_2.jpg differ
diff --git a/Results/ANN/Error_POD_Nodes_semilogy_L_infty.jpg b/Results/ANN/Error_POD_Nodes_semilogy_L_infty.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..bfeb634e8efb25d445dafc679040216c9da38136
Binary files /dev/null and b/Results/ANN/Error_POD_Nodes_semilogy_L_infty.jpg differ
diff --git a/Results/ANN/ErrorsSVD.xlsx b/Results/ANN/ErrorsSVD.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..1563dc91a1076cd465c40846bb1f3fa1ab338afb
Binary files /dev/null and b/Results/ANN/ErrorsSVD.xlsx differ
diff --git a/Results/ANN/OptimalSetup.npz b/Results/ANN/OptimalSetup.npz
new file mode 100644
index 0000000000000000000000000000000000000000..078c2d7943f54403e6c160ebe3095f69dc742f94
Binary files /dev/null and b/Results/ANN/OptimalSetup.npz differ
diff --git a/models/ANN.py b/models/ANN.py
new file mode 100644
index 0000000000000000000000000000000000000000..eb3978bd28895b60a4b9703b53007eabc1d0c490
--- /dev/null
+++ b/models/ANN.py
@@ -0,0 +1,100 @@
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.optimizers import Adam
+from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
+import matplotlib.pyplot as plt
+
+from models.RegressionModels import RegressionModels
+
+class ANN(RegressionModels):
+    def train(self, X_r_velocity, X_r_pressure, lr=1e-2, epochs=1000, batch_size=32, plot_learning=True):
+        # model_velocity, model_pressure = LinearRegression(), LinearRegression()
+        model_velocity = Sequential(
+            [
+                Dense(256, input_dim=self.param_matrix_train.shape[1], activation='relu'),
+                Dense(128, activation='relu'),
+                Dense(X_r_velocity.T.shape[1])
+            ]
+        )
+        model_pressure = Sequential(
+            [
+                Dense(256, input_dim=self.param_matrix_train.shape[1], activation='relu'),
+                Dense(128, activation='relu'),
+                Dense(X_r_pressure.T.shape[1])
+            ]
+        )
+
+        optimizer_velocity = Adam(learning_rate=lr)
+        optimizer_pressure = Adam(learning_rate=lr)
+        model_velocity.compile(
+            optimizer=optimizer_velocity,
+            loss='mean_squared_error', 
+            metrics = ['mae', 'mse'],
+        )
+        model_pressure.compile(
+            optimizer=optimizer_pressure,
+            loss='mean_squared_error', 
+            metrics = ['mae', 'mse'],
+        )
+
+        callbacks = [
+            EarlyStopping(monitor='mse', patience=250),
+            ReduceLROnPlateau(monitor='mse', factor=0.1, patience=100)
+        ]       
+        
+        print(f'X_r_velocity.shape: {X_r_velocity.shape}')
+        print(f'X_r_pressure.shape: {X_r_pressure.shape}')
+        print(f'self.param_matrix_train.shape: {self.param_matrix_train.shape}')
+        history_velocity = model_velocity.fit(
+            self.param_matrix_train, X_r_velocity.T, 
+            validation_split=0.2,
+            epochs=epochs, batch_size=batch_size,
+            callbacks=callbacks
+        )
+        history_pressure = model_pressure.fit(
+            self.param_matrix_train, X_r_pressure.T, 
+            validation_split=0.2,
+            epochs=epochs, batch_size=batch_size,
+            callbacks=callbacks
+        )
+
+        if plot_learning:
+            fig, axs = plt.subplots(ncols=2, figsize=(10, 5))
+            axs[0].semilogy(history_velocity.history['mae'], label='train')
+            axs[0].semilogy(history_velocity.history['val_mae'], label='validation')
+            axs[0].legend()
+            axs[0].set_ylabel('mae')
+            axs[0].set_xlabel('epoch')
+            axs[0].grid()
+
+            axs[1].semilogy(history_velocity.history['mse'], label='train')
+            axs[1].semilogy(history_velocity.history['val_mse'], label='validation')
+            axs[1].legend()
+            axs[1].set_ylabel('mse')
+            axs[1].set_xlabel('epoch')
+            axs[1].grid()
+
+            fig.tight_layout()
+            fig.savefig('models/trained_models/ANN/learning_curve_velocity.png')
+            fig.show()
+
+            fig, axs = plt.subplots(ncols=2, figsize=(10, 5))
+            axs[0].semilogy(history_pressure.history['mae'], label='train')
+            axs[0].semilogy(history_pressure.history['val_mae'], label='validation')
+            axs[0].legend()
+            axs[0].set_ylabel('mae')
+            axs[0].set_xlabel('epoch')
+            axs[0].grid()
+
+            axs[1].semilogy(history_pressure.history['mse'], label='train')
+            axs[1].semilogy(history_pressure.history['val_mse'], label='validation')
+            axs[1].legend()
+            axs[1].set_ylabel('mse')
+            axs[1].set_xlabel('epoch')
+            axs[1].grid()
+
+            fig.tight_layout()
+            fig.savefig('models/trained_models/ANN/learning_curve_pressure.png')
+            fig.show()
+
+        return model_velocity, model_pressure
diff --git a/models/__pycache__/ANN.cpython-312.pyc b/models/__pycache__/ANN.cpython-312.pyc
new file mode 100644
index 0000000000000000000000000000000000000000..dc4c97172bb9cb0b36876e86b00943f7cfadfee9
Binary files /dev/null and b/models/__pycache__/ANN.cpython-312.pyc differ
diff --git a/models/trained_models/ANN/learning_curve_pressure.png b/models/trained_models/ANN/learning_curve_pressure.png
new file mode 100644
index 0000000000000000000000000000000000000000..0f759eb788fdb042ecdcfde5863e0a1d1b0cbb3e
Binary files /dev/null and b/models/trained_models/ANN/learning_curve_pressure.png differ
diff --git a/models/trained_models/ANN/learning_curve_velocity.png b/models/trained_models/ANN/learning_curve_velocity.png
new file mode 100644
index 0000000000000000000000000000000000000000..2018803430f44d80519a7434333bcaf9474fd799
Binary files /dev/null and b/models/trained_models/ANN/learning_curve_velocity.png differ
diff --git a/models/trained_models/ANN/pressure.pkl b/models/trained_models/ANN/pressure.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0c3576aa3281520b9ff2a41b234a08a279a98868
Binary files /dev/null and b/models/trained_models/ANN/pressure.pkl differ
diff --git a/models/trained_models/ANN/pressure_1.pkl b/models/trained_models/ANN/pressure_1.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..8351d03859120bb25852fd0ed90ed9610232f4c6
Binary files /dev/null and b/models/trained_models/ANN/pressure_1.pkl differ
diff --git a/models/trained_models/ANN/pressure_21.pkl b/models/trained_models/ANN/pressure_21.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0c3576aa3281520b9ff2a41b234a08a279a98868
Binary files /dev/null and b/models/trained_models/ANN/pressure_21.pkl differ
diff --git a/models/trained_models/ANN/pressure_elbow.pkl b/models/trained_models/ANN/pressure_elbow.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..c1b6a93483bb27fba0be3a6876f88c64078db538
Binary files /dev/null and b/models/trained_models/ANN/pressure_elbow.pkl differ
diff --git a/models/trained_models/ANN/velocity.pkl b/models/trained_models/ANN/velocity.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..537ae24a6f1d03da1929f33df19d98be8f01e70d
Binary files /dev/null and b/models/trained_models/ANN/velocity.pkl differ
diff --git a/models/trained_models/ANN/velocity_1.pkl b/models/trained_models/ANN/velocity_1.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..0e87366dddbb73c859e5e86fce078728d6552d07
Binary files /dev/null and b/models/trained_models/ANN/velocity_1.pkl differ
diff --git a/models/trained_models/ANN/velocity_21.pkl b/models/trained_models/ANN/velocity_21.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..537ae24a6f1d03da1929f33df19d98be8f01e70d
Binary files /dev/null and b/models/trained_models/ANN/velocity_21.pkl differ
diff --git a/models/trained_models/ANN/velocity_elbow.pkl b/models/trained_models/ANN/velocity_elbow.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..09727af6f474baa97091a1ba8caeb4aafd3c6c48
Binary files /dev/null and b/models/trained_models/ANN/velocity_elbow.pkl differ