GPSINDy: Data-Driven Discovery of Equations of Motion

by   Junette Hsin, et al.

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.


page 1

page 2

page 3

page 4


Learning Stable Nonparametric Dynamical Systems with Gaussian Process Regression

Modelling real world systems involving humans such as biological process...

On the functional form of the radial acceleration relation

We apply a new method for learning equations from data – Exhaustive Symb...

Discovering Sparse Interpretable Dynamics from Partial Observations

Identifying the governing equations of a nonlinear dynamical system is k...

Please sign up or login with your details

Forgot password? Click here to reset