In this project we focus on training Physics-Informed Neural Networks with domain decomposition. Main focus of the work is to find entropy spable PINN which can handle shocks and most importantly discontinuities. Mostly the simulations would perform deep learning solutions of partial differential equations, mainly focusing on conservation laws. Additionally Bayesian inference and Bayesias Neural Networks are used for additional uncertainty quantification in time.