We answer the question: “Can Bayesian learning frameworks be integrated with Neural ODE’s to robustly quantify the uncertainty in the weights of a Neural ODE?” for the following categories of inference methods: (a) NUTS samples and stochastic frameworks like (b) SGLD, SGHMC. We test these methods on physical systems and ML datasets like MNIST. Finally, we demonstrate probabilistic, symbolic recovery of missing terms from dynamical systems using universal ODEs.