  We propose a new physics-informed neural network framework, IDPINN, based on
the enhancement of initialization and domain decomposition to improve
prediction accuracy. We train a PINN using a small dataset to obtain an initial
network structure, including the weighted matrix and bias, which initializes
the PINN for each subdomain. Moreover, we leverage the smoothness condition on
the interface to enhance the prediction performance. We numerically evaluated
it on several forward problems and demonstrated the benefits of IDPINN in terms
of accuracy.
