Gaussian process learning of nonlinear dynamics

arXiv:2312.12193v2 Announce Type: replace
Abstract: One of the pivotal tasks in scientific machine learning is to represent underlying dynamical systems from time series data. Many methods for such dynamics learning explicitly require the derivatives of state data, which are not directly available and can be approximated conventionally by finite differences. However, the discrete approximations of time derivatives may result in poor estimations when state data are scarce and/or corrupted by noise, thus compromising the predictiveness of the learned dynamical models. …

abstract arxiv cs.ce cs.lg cs.na data derivatives differences dynamics however machine machine learning math.na pivotal process scientific series state systems tasks time series type