A toolkit for data-driven discovery of governing equations in high-noise regimes. (arXiv:2111.04870v2 [cs.LG] UPDATED)

We consider the data-driven discovery of governing equations from time-series
data in the limit of high noise. The algorithms developed describe an extensive
toolkit of methods for circumventing the deleterious effects of noise in the
context of the sparse identification of nonlinear dynamics (SINDy) framework.
We offer two primary contributions, both focused on noisy data acquired from a
system x' = f(x). First, we propose, for use in high-noise settings, an
extensive toolkit of critically enabling extensions for the SINDy …

arxiv data discovery noise toolkit