Abstract
Recent years have seen rapid advances in the data-driven analysis of dynamical systems based on Koopman operator theory and related approaches. On the other hand, low-rank tensor product approximations – in particular the tensor train (TT) format – have become a valuable tool for the solution of large-scale problems in a number of fields. In this work, we combine Koopman-based models and the TT format, enabling their application to high-dimensional problems in conjunction with a rich set of basis functions or features. We derive efficient algorithms to obtain a reduced matrix representation of the system's evolution operator starting from an appropriate low-rank representation of the data. These algorithms can be applied to both stationary and non-stationary systems. We establish the infinite-data limit of these matrix representations, and demonstrate our methods’ capabilities using several benchmark data sets.
Original language | English |
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Article number | 133018 |
Journal | Physica D: Nonlinear Phenomena |
Volume | 427 |
Early online date | 6 Sept 2021 |
DOIs | |
Publication status | Published - Dec 2021 |
Keywords
- Dynamical systems
- Extended dynamic mode decomposition
- Koopman operator
- Molecular dynamics
- Tensor networks
- Tensor-train format
ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Mathematical Physics
- Condensed Matter Physics
- Applied Mathematics