27th Conference of the International Linear Algebra Society (ILAS 2026)

Tensor-based Dynamic Mode Decomposition for Complex Dynamics

May 20, 2026, 11:10 AM

25m

Torgersen Hall 1020 (Virginia Tech)

Minisymposium Talk Linear Algebra Foundations for Data-driven Modeling and Model Order Reduction Linear Algebra Foundations for Data-driven Modeling and Model Order Reduction

Speaker

Feliks Nueske (Max-Planck-Institute DCTS Magdeburg)

Description

In this talk, I will present recent work on analysing time series data for complex dynamics. Extended dynamic mode decomposition (EDMD, Williams et al, 2015) is a widely used algorithm to learn a linear surrogate model for the statistics of an evolving dynamics, based on the Koopman operator framework. For high-dimensional systems, choosing a suitable basis set can become challenging, as traditional methods are subject to the curse of dimensionality. Tensor-based versions of EDMD were first presented by Klus and co-workers in 2018, and then extended by Nüske et al in 2021. A further extension to approximate the infinitesimal generator of the dynamics was developed by Lücke et al in 2022.

In this talk, I will provide an overview of these developments, and then present some recent results on the application of tensor-based EDMD to simulation data of complex bio-molecules. In particular, I will show how combining dimensionality reduction methods with tensor-based models helps unravel complex structure in high-dimensional time series data.