1.1k citations · 5.5k across the 44 of their papers we have counts for
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Deeptime: a Python library for machine learning dynamical models from time series data
Moritz Hoffmann, Martin Scherer, Tim Hempel +8
Generation and analysis of time-series data is relevant to many quantitative fields ranging from economics to fluid mechanics. In the physical sciences, structures such as metastab…
Kernel methods for detecting coherent structures in dynamical data
Stefan Klus, Brooke E. Husic, Mattes Mollenhauer +1
We illustrate relationships between classical kernel-based dimensionality reduction techniques and eigendecompositions of empirical estimates of reproducing kernel Hilbert space (R…
Optimal data-driven estimation of generalized Markov state models for non-equilibrium dynamics
Péter Koltai, Hao Wu, Frank Noé +1
There are multiple ways in which a stochastic system can be out of statistical equilibrium. It might be subject to time-varying forcing; or be in a transient phase on its way towar…
Data-driven model reduction and transfer operator approximation
Stefan Klus, Feliks Nüske, Péter Koltai +4
In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to ap…