Schaller M, Worthmann K, Philipp F, Peitz S, Nüske F. Towards reliable data-based optimal and predictive control using extended DMD. In: IFAC-PapersOnLine. Vol 56. ; 2023:169-174. doi:10.1016/j.ifacol.2023.02.029

Nüske F, Peitz S, Philipp F, Schaller M, Worthmann K. Finite-data error bounds for Koopman-based prediction and control. Journal of Nonlinear Science. 2023;33. doi:10.1007/s00332-022-09862-1

Klus S, Nüske F, Peitz S. Koopman analysis of quantum systems. Journal of Physics A: Mathematical and Theoretical. 2022;55(31):314002. doi:10.1088/1751-8121/ac7d22

Nüske F, Gelß P, Klus S, Clementi C. Tensor-based computation of metastable and coherent sets. Physica D: Nonlinear Phenomena. Published online 2021. doi:10.1016/j.physd.2021.133018

Klus S, Gelß P, Nüske F, Noé F. Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry. Machine Learning: Science and Technology. Published online 2021. doi:10.1088/2632-2153/ac14ad

Nüske F, Koltai P, Boninsegna L, Clementi C. Spectral Properties of Effective Dynamics from Conditional Expectations. Entropy. 2021. doi:10.3390/e23020134

Klus S, Nüske F, Hamzi B. Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. Entropy. 2020. doi:10.3390/e22070722

Klus S, Nüske F, Peitz S, Niemann J-H, Clementi C, Schütte C. Data-driven approximation of the Koopman generator: Model reduction, system identification, and control. Physica D: Nonlinear Phenomena. 2020;406. doi:10.1016/j.physd.2020.132416

Nüske F, Boninsegna L, Clementi C. Coarse-graining molecular systems by spectral matching. The Journal of Chemical Physics. 2019. doi:10.1063/1.5100131

Litzinger F, Boninsegna L, Wu H, et al. Rapid Calculation of Molecular Kinetics Using Compressed Sensing. Journal of Chemical Theory and Computation. 2018:2771-2783. doi:10.1021/acs.jctc.8b00089

Klus S, Nüske F, Koltai P, et al. Data-Driven Model Reduction and Transfer Operator Approximation. Journal of Nonlinear Science. 2018:985-1010. doi:10.1007/s00332-017-9437-7

Boninsegna L, Nüske F, Clementi C. Sparse learning of stochastic dynamical equations. The Journal of Chemical Physics. 2018. doi:10.1063/1.5018409

Hruska E, Abella JR, Nüske F, Kavraki LE, Clementi C. Quantitative comparison of adaptive sampling methods for protein dynamics. The Journal of Chemical Physics. 2018. doi:10.1063/1.5053582

Nüske F, Wu H, Prinz J-H, Wehmeyer C, Clementi C, Noé F. Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias. The Journal of Chemical Physics. 2017. doi:10.1063/1.4976518

Wu H, Nüske F, Paul F, Klus S, Koltai P, Noé F. Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations. The Journal of Chemical Physics. 2017. doi:10.1063/1.4979344

Nüske F, Schneider R, Vitalini F, Noé F. Variational tensor approach for approximating the rare-event kinetics of macromolecular systems. The Journal of Chemical Physics. 2016. doi:10.1063/1.4940774

Nüske F, Keller BG, Pérez-Hernández G, Mey ASJS, Noé F. Variational Approach to Molecular Kinetics. Journal of Chemical Theory and Computation. 2014:1739-1752. doi:10.1021/ct4009156

Noé F, Nüske F. A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. Multiscale Modeling &; Simulation. 2013:635-655. doi:10.1137/110858616