We present a new scientific machine learning method that learns from data computationally inexpensive surrogate model for predicting the evolution of system governed by time-dependent nonlinear partial differential equation (PDE), an enabling technology many computational algorithms used in engineering settings. Our formulation generalizes to function space PDE setting Operator Inference previo...