A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. - Frank Noé; Feliks Nüske (Multiscale Model. Simul., 2013)

Data-Driven Model Reduction and Transfer Operator Approximation. - Stefan Klus; Feliks Nüske; Péter Koltai; Hao Wu 035; Ioannis G. Kevrekidis; Christof Schütte; Frank Noé (J. Nonlinear Sci., 2018)

Tensor-based EDMD for the Koopman analysis of high-dimensional systems. - Feliks Nüske; Patrick Gelß; Stefan Klus; Cecilia Clementi (CoRR, 2019)

Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. - Stefan Klus; Feliks Nüske; Boumediene Hamzi (Entropy, 2020)

Spectral Properties of Effective Dynamics from Conditional Expectations. - Feliks Nüske; Péter Koltai; Lorenzo Boninsegna; Cecilia Clementi (Entropy, 2021)

Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. - Stefan Klus; Patrick Gelß; Feliks Nüske; Frank Noé (Mach. Learn. Sci. Technol., 2021)

tgEDMD: Approximation of the Kolmogorov Operator in Tensor Train Format. - Marvin Lücke; Feliks Nüske (J. Nonlinear Sci., 2022)

Partial observations, coarse graining and equivariance in Koopman operator theory for large-scale dynamical systems. - Sebastian Peitz; Hans Harder; Feliks Nüske; Friedrich Philipp; Manuel Schaller; Karl Worthmann (CoRR, 2023)

Finite-Data Error Bounds for Koopman-Based Prediction and Control. - Feliks Nüske; Sebastian Peitz; Friedrich Philipp; Manuel Schaller; Karl Worthmann (J. Nonlinear Sci., 2023)

Funnel control for Langevin dynamics. - Thomas Berger; Feliks Nüske (Autom., 2025)

Error Analysis of Kernel EDMD for Prediction and Control in the Koopman Framework. - Friedrich M. Philipp; Manuel Schaller; Karl Worthmann; Sebastian Peitz; Feliks Nüske (J. Nonlinear Sci., 2025)