Structure-preserving Sparse Identification of Nonlinear Dynamics for
Data-driven Modeling
- URL: http://arxiv.org/abs/2109.05364v1
- Date: Sat, 11 Sep 2021 20:32:10 GMT
- Title: Structure-preserving Sparse Identification of Nonlinear Dynamics for
Data-driven Modeling
- Authors: Kookjin Lee, Nathaniel Trask, Panos Stinis
- Abstract summary: We present a unification of the Sparse Identification of 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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