Deep Koopman Learning using Noisy Data
This addresses the challenge of approximating state evolution in dynamical systems under noisy observations, which is incremental as it builds on existing deep Koopman methods.
This paper tackles the problem of learning a Koopman operator for dynamical systems from noisy data by proposing a method that only requires bounded measurement noise and modifies existing deep Koopman formulations to mitigate noise effects, demonstrating performance on standard benchmarks and comparing with recent literature.
This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under noisy observations. To this end, our proposed solution has two main advantages. First, the proposed method only requires the measurement noise to be bounded. Second, the proposed method modifies the existing deep Koopman operator formulations by characterizing the effect of the measurement noise on the Koopman operator learning and then mitigating it by updating the tunable parameter of the observable functions of the Koopman operator, making it easy to implement. The performance of the proposed method is demonstrated on several standard benchmarks. We then compare the presented method with similar methods proposed in the latest literature on Koopman learning.