Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling [article]

Kookjin Lee, Nathaniel Trask, Panos Stinis
2021 arXiv   pre-print
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
more » ... t formalisms for both reversible and irreversible dynamics. We present a suite of benchmarks demonstrating effectiveness and structure preservation, including for chaotic systems.
arXiv:2109.05364v1 fatcat:letswnlnxnektob25kr7thq4jm