np_train_f_t = np.append(np_train_f_t, np_t)
if np_train_f_v is None:
np_train_f_v = np_v
else:
np_train_f_v = np.append(np_train_f_v, np_v)
if np_train_f_ic is None:
np_train_f_ic = np_ic
else:
np_train_f_ic = np.append(np_train_f_ic, np_ic)
# Normalizers
t_normalizer = Normalizer()
v_normalizer = Normalizer()
i_normalizer = Normalizer()
t_normalizer.parametrize(np_t)
v_normalizer.parametrize(np.array(train_vs))
i_normalizer.parametrize(np.array(train_ics))
# Train data normalization
np_norm_train_u_t = t_normalizer.normalize(np_train_u_t)
np_norm_train_u_v = v_normalizer.normalize(np_train_u_v)
np_norm_train_u_ic = i_normalizer.normalize(np_train_u_ic)
np_norm_train_f_t = t_normalizer.normalize(np_train_f_t)
np_norm_train_f_v = v_normalizer.normalize(np_train_f_v)
np_norm_train_f_ic = i_normalizer.normalize(np_train_f_ic)
# PINN instancing
hidden_layers = [9, 9]
learning_rate = 0.001
