  In this paper, we address the challenge of deriving dynamical models from
sparse and noisy data. High-quality data is crucial for symbolic regression
algorithms; limited and noisy data can present modeling challenges. To overcome
this, we combine Gaussian process regression with a sparse identification of
nonlinear dynamics (SINDy) method to denoise the data and identify nonlinear
dynamical equations. Our simple approach offers improved robustness with
sparse, noisy data compared to SINDy alone. We demonstrate its effectiveness on
a Lotka-Volterra model, a unicycle dynamic model in simulation, and hardware
data from an NVIDIA JetRacer system. We show superior performance over
baselines including 20.78% improvement over SINDy and 61.92% improvement over
SSR in predicting future trajectories from discovered dynamics.
