Abstract
Discovery of dynamical systems from data forms the foundation for data-driven modeling and recently, structure-preserving geometric perspectives have been shown to provide improved forecasting, stability, and physical realizability guarantees. We present here a unification of the Sparse Identification of Nonlinear Dynamics (SINDy) formalism with neural ordinary differential equations. The resulting framework allows learning of both “black-box” dynamics and learning of structure preserving bracket formalisms for both reversible and irreversible dynamics. We present a suite of benchmarks demonstrating effectiveness and structure preservation, including for chaotic systems.
Original language | English (US) |
---|---|
Pages (from-to) | 65-80 |
Number of pages | 16 |
Journal | Proceedings of Machine Learning Research |
Volume | 190 |
State | Published - 2022 |
Event | 3rd Annual Conference on Mathematical and Scientific Machine Learning, MSML 2022 - Beijing, China Duration: Aug 15 2022 → Aug 17 2022 |
Keywords
- GENERIC
- Structure preservation
- System Identification
- neural ordinary differential equations
ASJC Scopus subject areas
- Artificial Intelligence
- Software
- Control and Systems Engineering
- Statistics and Probability