Scientific machine learning is a novel discipline comprising applications of machine learning techniques to efficiently tackle challenges from science and engineering domains. Physics informed neural networksPhysics-Informed Neural Networks (PINNs) (PINNs) are one of the major workhorses pertaining to scientific machine learning, which combines the universal approximation capabilities of a neural network with that of physical information in the form of differential equations. The current chapter gives an overview of neural networks and deep-learning before proceeding to the details of PINNsPhysics-Informed Neural Networks (PINNs) and their application to phase field modeling, one of the potential methods in analyzing multi-phase materials.