Sparse Identification of Slow Timescale Dynamics

Understand

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.