We introduce a kernel-based framework for inferring ordinary and partial differential equations from sparse, partial observations of solution-source pairs. The proposed approach comes with simple and transparent convergence, and a priori error estimates guarantees. This presentation is based on a joint work with Bamdad Hosseini, Alexander Hsu, Yasamin Jalalian, and Juan Osorio.