Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data

arXiv:2402.15357v1 Announce Type: cross
Abstract: We propose a fast probabilistic framework for identifying differential equations governing the dynamics of observed data. We recast the SINDy method within a Bayesian framework and use Gaussian approximations for the prior and likelihood to speed up computation. The resulting method, Bayesian-SINDy, not only quantifies uncertainty in the parameters estimated but also is more robust when learning the correct model from limited and noisy data. Using both synthetic and real-life examples such as Lynx-Hare population …

abstract arxiv bayesian computation data differential dynamics framework identification likelihood nlin.cd prior speed stat.me stat.ml type