CINDy: Conditional gradient-based Identification of Non-linear Dynamics -- Noise-robust recovery

Understand

Governing equations are essential to the study of nonlinear dynamics, often enabling the prediction of previously unseen behaviors as well as the inclusion into control strategies.

• The discovery of governing equations from data thus has the potential to transform data-rich fields where well-established dynamical models remain unknown.
• This work contributes to the recent trend in data-driven sparse identification of nonlinear dynamics of finding the best sparse fit to observational data in a large library of potential nonlinear models.
• We propose an efficient first-order Conditional Gradient algorithm for solving the underlying optimization problem.