Noé, F.; Nüske, F.: A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems. Multiscale Modeling & Simulation 11, pp. 635 - 655 (2013)
Nüske, F.; Keller, B. G.; Pérez-Hernández, G.; Mey, A. S. J. S.; Noé, F.: Variational Approach to Molecular Kinetics. Journal of Chemical Theory and Computation 10, pp. 1739 - 1752 (2014)
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 144, 054105 (2016)
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 146, 094104 (2017)
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 146, 154104 (2017)
Boninsegna, L.; Nüske, F.; Clementi, C.: Sparse learning of stochastic dynamical equations. The Journal of Chemical Physics 148, 241723 (2018)
Hruska, E.; Abella, J. R.; Nüske, F.; Kavraki, L. E.; Clementi, C.: Quantitative comparison of adaptive sampling methods for protein dynamics. The Journal of Chemical Physics 149, 244119 (2018)
Klus, S.; Nüske, F.; Koltai, P.; Wu, H.; Kevrekidis, I.; Schütte, C.; Noé, F.: Data-Driven Model Reduction and Transfer Operator Approximation. Journal of Nonlinear Science 28, pp. 985 - 1010 (2018)
Litzinger, F.; Boninsegna, L.; Wu, H.; Nüske, F.; Patel, R.; Baraniuk, R.; Noé, F.; Clementi, C.: Rapid Calculation of Molecular Kinetics Using Compressed Sensing. Journal of Chemical Theory and Computation 14, pp. 2771 - 2783 (2018)
Nüske, F.; Boninsegna, L.; Clementi, C.: Coarse-graining molecular systems by spectral matching. The Journal of Chemical Physics 151, 044116 (2019)
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 406, 132416 (2020)
Klus, S.; Nüske, F.; Hamzi, B.: Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator. Entropy 22, 722 (2020)
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 2, 045016 (2021)
Nüske, F.; Gelß, P.; Klus, S.; Clementi, C.: Tensor-based computation of metastable and coherent sets. Physica D: Nonlinear Phenomena 427, 133018 (2021)
Nüske, F.; Koltai, P.; Boninsegna, L.; Clementi, C.: Spectral Properties of Effective Dynamics from Conditional Expectations. Entropy 23, 134 (2021)
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