M. Schaller, K. Worthmann, F. Philipp, S. Peitz, and F. Nüske, “Towards reliable data-based optimal and predictive control using extended DMD,”10.1016/j.ifacol.2023.02.029__in IFAC-PapersOnLine, 2023, vol. 56, no. 1, pp. 169–174, doi:10.1016/j.ifacol.2023.02.029 .

F. Nüske, S. Peitz, F. Philipp, M. Schaller, and K. Worthmann, “Finite-data error bounds for Koopman-based prediction and control,”10.1007/s00332-022-09862-1__Journal of Nonlinear Science, vol. 33, Art. no. 14, 2023, doi:10.1007/s00332-022-09862-1 .

S. Klus, F. Nüske, and S. Peitz, “Koopman analysis of quantum systems,”10.1088/1751-8121/ac7d22__Journal of Physics A: Mathematical and Theoretical, vol. 55, no. 31, p. 314002, 2022, doi:10.1088/1751-8121/ac7d22 .

F. Nüske, P. Gelß, S. Klus, and C. Clementi, “Tensor-based computation of metastable and coherent sets,”10.1016/j.physd.2021.133018__Physica D: Nonlinear Phenomena, Art. no. 133018, 2021, doi:10.1016/j.physd.2021.133018 .

S. Klus, P. Gelß, F. Nüske, and F. Noé, “Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry,”10.1088/2632-2153/ac14ad__Machine Learning: Science and Technology, Art. no. 045016, 2021, doi:10.1088/2632-2153/ac14ad .

F. Nüske, P. Koltai, L. Boninsegna, and C. Clementi, “Spectral Properties of Effective Dynamics from Conditional Expectations,” Entropy, 2021.

S. Klus, F. Nüske, and B. Hamzi, “Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator,” Entropy, 2020.

S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, and C. Schütte, “Data-driven approximation of the Koopman generator: Model reduction, system identification, and control,” Physica D: Nonlinear Phenomena, vol. 406, 2020.

F. Nüske, L. Boninsegna, and C. Clementi, “Coarse-graining molecular systems by spectral matching,” The Journal of Chemical Physics, 2019.

F. Litzinger et al., “Rapid Calculation of Molecular Kinetics Using Compressed Sensing,” Journal of Chemical Theory and Computation, pp. 2771–2783, 2018.

S. Klus et al., “Data-Driven Model Reduction and Transfer Operator Approximation,” Journal of Nonlinear Science, pp. 985–1010, 2018.

L. Boninsegna, F. Nüske, and C. Clementi, “Sparse learning of stochastic dynamical equations,” The Journal of Chemical Physics, 2018.

E. Hruska, J. R. Abella, F. Nüske, L. E. Kavraki, and C. Clementi, “Quantitative comparison of adaptive sampling methods for protein dynamics,” The Journal of Chemical Physics, 2018.

F. Nüske, H. Wu, J.-H. Prinz, C. Wehmeyer, C. Clementi, and F. Noé, “Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias,” The Journal of Chemical Physics, 2017.

H. Wu, F. Nüske, F. Paul, S. Klus, P. Koltai, and F. Noé, “Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations,” The Journal of Chemical Physics, 2017.

F. Nüske, R. Schneider, F. Vitalini, and F. Noé, “Variational tensor approach for approximating the rare-event kinetics of macromolecular systems,” The Journal of Chemical Physics, 2016.

F. Nüske, B. G. Keller, G. Pérez-Hernández, A. S. J. S. Mey, and F. Noé, “Variational Approach to Molecular Kinetics,” Journal of Chemical Theory and Computation, pp. 1739–1752, 2014.

F. Noé and F. Nüske, “A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems,” Multiscale Modeling &; Simulation, pp. 635–655, 2013.