A unified approach for sparse dynamical system inference from temporal measurements.

ABSTRACT: MOTIVATION:Temporal variations in biological systems and more generally in natural sciences are typically modeled as a set of ordinary, partial or stochastic differential or difference equations. Algorithms for learning the structure and the parameters of a dynamical system are distinguished based on whether time is discrete or continuous, observations are time-series or time-course and whether the system is deterministic or stochastic, however, there is no approach able to handle the various types of dynamical systems simultaneously. RESULTS:In this paper, we present a unified approach to infer both the structure and the parameters of non-linear dynamical systems of any type under the restriction of being linear with respect to the unknown parameters. Our approach, which is named Unified

SUBMITTER: Pantazis Y 

PROVIDER: S-EPMC6748758 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature