Sparse Identification of Dynamics
SINDy and operator learning from data.
Sparse Identification of Dynamics. SINDy and operator learning from data.
Recent technical contributions
A handful of recent papers carry the methodological frontier of sparse identification of dynamics forward. Discovering governing equations from data by sparse identification of nonlinear dynamical systems (Brunton-2016b et al., 2016) is a primary reference for this area and develops new techniques or results that downstream work builds on.
Open methodological questions for sparse identification of dynamics include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- paper · primary · 2016brunton-2016b, proctor-2016b, kutz-2016b
In context
Where this topic sits in the prerequisite graph. Click any node to jump.
Review this topic
This page was drafted by an agent and is waiting on expert review. Spotted a wrong prerequisite, a missing concept, a misattributed source, or a factual slip? Tell us — your review opens a tracked issue maintainers act on.