We present two approaches to system identification,i.e.the identification of partial differential equations (PDEs) from measurement data.The first is a regression-based variational system identification procedure that is advantageous in not requiring repeated forward model solves and has good scalability to large number of differential operators.However it has strict data type requirements needing the ability to directly represent the operators through the available data.The second is a Bayesian inference framework highly valuable for providing uncertainty quantification,and flexible for accommodating sparse and noisy data that may also be indirect quantities of interest.However,it also requires repeated forward solutions of the PDE models which is expensive and hinders scalability.We provide illustrations of results on a model problem for patt formation dynamics,and discuss merits of the presented methods.