Author

F. Nüske

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#machine learning Preprint Oct 2026

Bilevel optimization for data-driven learning of Koopman embeddings using kernel-based autoencoders

Koopman operator theory provides a linear framework for analyzing nonlinear dynamical systems and has become a major tool for data-driven modeling. A central challenge, however, is that finite-dimensional approximations computed by methods such as extended dynamic mode decomposition (EDMD) require the dictionary to be...

J. N. N'konzi, F. Nüske, Stefan Klus · 0 citations

#protein folding Preprint Sep 2026

Tensor-based Approximation of Molecular Kinetics: Generator Learning, Reaction Coordinates and Incremental Updating

An approach to analyze long-timescale kinetics of molecular dynamics simulations - meta-stable states and transition timescales - by a tensor-based approximation of the infinitesimal generator is presented, and the number of reaction coordinates can be gradually increased without having to solve the variational problem...

Minakshi Verma, Peter Benner, F. Nüske · 0 citations