Symbolic Regression on Sparse and Noisy Data with Gaussian Processes

arXiv:2309.11076v2 Announce Type: replace
Abstract: 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 …

abstract algorithms arxiv challenge challenges cs.lg cs.sy data eess.sy gaussian processes identification modeling paper process processes quality quality data regression type