Bayesian Dynamic Mode Decomposition with Variational Matrix Factorization

4Citations
Citations of this article
12Readers
Mendeley users who have this article in their library.

Abstract

Dynamic mode decomposition (DMD) and its extensions are data-driven methods that have substantially contributed to our understanding of dynamical systems. However, because DMD and most of its extensions are deterministic, it is difficult to treat probabilistic representations of parameters and predictions. In this work, we propose a novel formulation of a Bayesian DMD model. Our Bayesian DMD model is consistent with the procedure of standard DMD, which is to first determine the subspace of observations, and then compute the modes on that subspace. Variational matrix factorization makes it possible to realize a fully-Bayesian scheme of DMD. Moreover, we derive a Bayesian DMD model for incomplete data, which demonstrates the advantage of probabilistic modeling. Finally, both of nonlinear simulated and real-world datasets are used to illustrate the potential of the proposed method.

Cite

CITATION STYLE

APA

Kawashima, T., Shouno, H., & Hino, H. (2021). Bayesian Dynamic Mode Decomposition with Variational Matrix Factorization. In 35th AAAI Conference on Artificial Intelligence, AAAI 2021 (Vol. 9B, pp. 8083–8091). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v35i9.16985

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free