Model free data assimilation with Takens embedding

TL;DR

This paper introduces a data-driven, model-free approach for state estimation in dynamical systems using Takens' embedding theorem, surrogate dynamics, and ensemble Kalman filtering, applicable without knowledge of the underlying system.

Contribution

It develops a novel framework combining Takens' theorem, dynamic mode decomposition, and ensemble Kalman filtering for state estimation without explicit system models.

Findings

Accurately estimates state distribution without physical system knowledge

Uses surrogate dynamics learned from noisy data

Applicable to chaotic systems with nonparametric methods

Abstract

In many practical scenarios, the dynamical system is not available and standard data assimilation methods are not applicable. Our objective is to construct a data-driven model for state estimation without the underlying dynamics. Instead of directly modeling the observation operator with noisy observation, we establish the state space model of the denoised observation. Through data assimilation techniques, the denoised observation information could be used to recover the original model state. Takens' theorem shows that an embedding of the partial and denoised observation is diffeomorphic to the attractor. This gives a theoretical base for estimating the model state using the reconstruction map. To realize the idea, the procedure consists of offline stage and online stage. In the offline stage, we construct the surrogate dynamics using dynamic mode decomposition with noisy snapshots to learn the transition operator for the denoised observation. The filtering distribution of the denoised observation can be estimated using adaptive ensemble Kalman filter, without knowledge of the model error and observation noise covariances. Then the reconstruction map can be established using the posterior mean of the embedding and its corresponding state. In the online stage, the observation is filtered with the surrogate dynamics. Then the online state estimation can be performed utilizing the reconstruction map and the filtered observation. Furthermore, the idea can be generalized to the nonparametric framework with nonparametric time series prediction methods for chaotic problems. The numerical results show the proposed method can estimate the state distribution without the physical dynamical system.