Update RNA-Keras-Ex2.py

Equation-Chaleur/Eq-Chal-RNA.py

@@ -0,0 +1,34 @@
+import numpy as np
+from keras.models import Sequential
+from keras.layers import Dense
+
+# Constants
+dx = 0.1
+dt = 0.01
+alpha = 0.1
+
+# Initialize the temperature grid
+T = np.zeros((100, 100))
+T[:, 0] = 100  # Left edge is 100 degrees
+T[:, -1] = 0  # Right edge is 0 degrees
+
+# Build the neural network model
+model = Sequential()
+model.add(Dense(100, input_shape=(100,), activation='relu'))
+model.add(Dense(100, activation='relu'))
+model.add(Dense(100))
+model.compile(loss='mse', optimizer='adam')
+
+# Iterate over time steps
+for t in range(100):
+  # Compute the new temperature grid
+  T_new = np.zeros_like(T)
+  for i in range(1, 99):
+    for j in range(1, 99):
+      T_new[i, j] = T[i, j] + alpha * dt / dx**2 * (T[i+1, j] - 2*T[i, j] + T[i-1, j] + T[i, j+1] - 2*T[i, j] + T[i, j-1])
+
+  # Use the neural network to predict the temperature at the next time step
+  predictions = model.predict(T_new)
+
+  # Update the temperature grid with the predicted values
+  T = predictions

RNA-Keras-Exemple/RNA-Keras-Ex2.py

@@ -9,7 +9,7 @@
 # Création du modèle de réseau de neurones en utilisant la bibliothèque Keras
 
 model = Sequential()
-model.add(Dense(4, input_dim=2, activation='relu'))
+model.add(Dense(5, input_dim=2, activation='relu'))
 model.add(Dense(1, activation='sigmoid'))
 
 # Compilation et entraînement du modèle en utilisant l'algorithme de descente de gradient