In this paper, we consider the problem of discovering dynamical system models from noisy data. The presence of noise is known to be a significant problem for symbolic regression algorithms. We combine Gaussian process regression, a nonparametric learning method, with SINDy, a parametric learning approach, to identify nonlinear dynamical systems from data. The key advantages of our proposed approach are its simplicity coupled with the fact that it demonstrates improved robustness properties with noisy data over SINDy. We demonstrate our proposed approach on a Lotka-Volterra model and a unicycle dynamic model in simulation and on an NVIDIA JetRacer system using hardware data. We demonstrate improved performance over SINDy for discovering the system dynamics and predicting future trajectories.

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