AA1-Life Sciences



Learning Transition Manifolds and Effective Dynamics of Biomolecules

Project Heads

Péter Koltai, Stefan Klus, Gitta Kutyniok, Klaus-Robert Müller, Christof Schütte

Project Members

Mattes Mollenhauer (FU)

Project Duration

01.01.2019 – 31.12.2021

Located at

FU Berlin / ZIB


The transition pathways underlying molecular function are essentially supported by a rather low dimensional manifold. Recent results show how to find such transition manifolds (TM) by embedding techniques. The project aims at finding algorithms for TM learning with quantitative error estimates and for inferring the related effective dynamics by linking ideas from compressed sensing and deep learning to available embedding results.

Project Webpages

Selected Publications

  1. M. Mollenhauer, S. Klus, Ch. Schütte, and P. Koltai. “Kernel autocovariance operators of stationary processes: estimation and convergence.” Preprint: arXiv: 2004.00891. 2020.
  2. A. Bittracher and Ch. Schütte. “A weak characterization of slow variables in stochastic dynamical systems.” Preprint: arXiv: 2005.01631. 2020.
  3. A. Bittracher, S. Klus, B. Hamzi, P. Koltai, and Ch. Schütte. “Dimensionality Reduction of Complex Metastable Systems via Kernel Embeddings of Transition Manifolds.” Preprint arXiv:1904.08622. 2019.
  4. I. Schuster, M. Mollenhauer, S. Klus, and K. Muandet. Kernel Conditional Density Operators. Accepted for AISTATS, 2020.
  5. S. Klus, B. E. Husic, M. Mollenhauer, and F. Noé. Kernel methods for detecting coherent structures in dynamical data. Chaos, 2019.
  6. M. Mollenhauer, I. Schuster, S. Klus, and C. Schütte. Singular Value Decomposition of Operators on Reproducing Kernel Hilbert Spaces. Preprint: arXiv: 1807.09331. 2018.

Selected Pictures

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