Sparse Identification of Slow Timescale Dynamics
From MaRDI portal
Publication:6341821
arXiv2006.00940MaRDI QIDQ6341821
Author name not available (Why is that?)
Publication date: 1 June 2020
Abstract: Multiscale phenomena that evolve on multiple distinct timescales are prevalent throughout the sciences. It is often the case that the governing equations of the persistent and approximately periodic fast scales are prescribed, while the emergent slow scale evolution is unknown. Yet the course-grained, slow scale dynamics is often of greatest interest in practice. In this work we present an accurate and efficient method for extracting the slow timescale dynamics from signals exhibiting multiple timescales that are amenable to averaging. The method relies on tracking the signal at evenly-spaced intervals with length given by the period of the fast timescale, which is discovered using clustering techniques in conjunction with the dynamic mode decomposition. Sparse regression techniques are then used to discover a mapping which describes iterations from one data point to the next. We show that for sufficiently disparate timescales this discovered mapping can be used to discover the continuous-time slow dynamics, thus providing a novel tool for extracting dynamics on multiple timescales.
Has companion code repository: https://github.com/jbramburger/Slow-Discovery
This page was built for publication: Sparse Identification of Slow Timescale Dynamics
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6341821)
