# Flatten the data
X_flat = X.flatten()
Y_flat = Y.flatten()
Z_flat = Z.flatten()

# Stack X and Y as input features
inputs = np.column_stack((X_flat, Y_flat))
outputs = Z_flat

# Normalize the inputs and outputs
inputs_mean = np.mean(inputs, axis=0)
inputs_std = np.std(inputs, axis=0)
outputs_mean = np.mean(outputs)
outputs_std = np.std(outputs)

inputs = (inputs - inputs_mean) / inputs_std
outputs = (outputs - outputs_mean) / outputs_std