Discovery of Dynamics Using Linear Multistep Methods
This provides a theoretical foundation for data-driven dynamics modeling, though it is incremental as it adapts existing numerical methods to a new context.
The paper tackles the inverse problem of learning dynamics from state data by repurposing linear multistep methods, establishing a rigorous convergence framework and identifying specific convergent schemes like Adams-Bashforth for M=1-6 and Backwards Differentiation Formula for all positive M.
Linear multistep methods (LMMs) are popular time discretization techniques for the numerical solution of differential equations. Traditionally they are applied to solve for the state given the dynamics (the forward problem), but here we consider their application for learning the dynamics given the state (the inverse problem). This repurposing of LMMs is largely motivated by growing interest in data-driven modeling of dynamics, but the behavior and analysis of LMMs for discovery turn out to be significantly different from the well-known, existing theory for the forward problem. Assuming a highly idealized setting of being given the exact state with a zero residual of the discrete dynamics, we establish for the first time a rigorous framework based on refined notions of consistency and stability to yield convergence using LMMs for discovery. When applying these concepts to three popular $M-$step LMMs, the Adams-Bashforth, Adams-Moulton, and Backwards Differentiation Formula schemes, the new theory suggests that Adams-Bashforth for $M$ ranging from $1$ and $6$, Adams-Moulton for $M=0$ and $M=1$, and Backwards Differentiation Formula for all positive $M$ are convergent, and, otherwise, the methods are not convergent in general. In addition, we provide numerical experiments to both motivate and substantiate our theoretical analysis.