Towards reliable data-based optimal and predictive control using extended DMD
M. Schaller, K. Worthmann, F. Philipp, S. Peitz, F. Nüske, in: IFAC-PapersOnLine, 2023, pp. 169–174.
LibreCat: /record/30125
DOI: 10.1016/j.ifacol.2023.02.029
arXiv: 2202.09084

Finite-data error bounds for Koopman-based prediction and control
F. Nüske, S. Peitz, F. Philipp, M. Schaller, K. Worthmann, Journal of Nonlinear Science 33 (2023).
LibreCat: /record/23428
DOI: 10.1007/s00332-022-09862-1

Koopman analysis of quantum systems
S. Klus, F. Nüske, S. Peitz, Journal of Physics A: Mathematical and Theoretical 55 (2022) 314002.
LibreCat: /record/29673
DOI: 10.1088/1751-8121/ac7d22
arXiv: 2201.12062

Tensor-based computation of metastable and coherent sets
F. Nüske, P. Gelß, S. Klus, C. Clementi, Physica D: Nonlinear Phenomena (2021).
LibreCat: /record/24169
DOI: 10.1016/j.physd.2021.133018

Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
S. Klus, P. Gelß, F. Nüske, F. Noé, Machine Learning: Science and Technology (2021).
LibreCat: /record/24170
DOI: 10.1088/2632-2153/ac14ad

Spectral Properties of Effective Dynamics from Conditional Expectations
F. Nüske, P. Koltai, L. Boninsegna, C. Clementi, Entropy (2021).
LibreCat: /record/21820
DOI: 10.3390/e23020134

Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator
S. Klus, F. Nüske, B. Hamzi, Entropy (2020).
LibreCat: /record/21819
DOI: 10.3390/e22070722

Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
S. Klus, F. Nüske, S. Peitz, J.-H. Niemann, C. Clementi, C. Schütte, Physica D: Nonlinear Phenomena 406 (2020).
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Coarse-graining molecular systems by spectral matching
F. Nüske, L. Boninsegna, C. Clementi, The Journal of Chemical Physics (2019).
LibreCat: /record/21944
DOI: 10.1063/1.5100131

Rapid Calculation of Molecular Kinetics Using Compressed Sensing
F. Litzinger, L. Boninsegna, H. Wu, F. Nüske, R. Patel, R. Baraniuk, F. Noé, C. Clementi, Journal of Chemical Theory and Computation (2018) 2771–2783.
LibreCat: /record/21940
DOI: 10.1021/acs.jctc.8b00089

Data-Driven Model Reduction and Transfer Operator Approximation
S. Klus, F. Nüske, P. Koltai, H. Wu, I. Kevrekidis, C. Schütte, F. Noé, Journal of Nonlinear Science (2018) 985–1010.
LibreCat: /record/21941
DOI: 10.1007/s00332-017-9437-7

Sparse learning of stochastic dynamical equations
L. Boninsegna, F. Nüske, C. Clementi, The Journal of Chemical Physics (2018).
LibreCat: /record/21942
DOI: 10.1063/1.5018409

Quantitative comparison of adaptive sampling methods for protein dynamics
E. Hruska, J.R. Abella, F. Nüske, L.E. Kavraki, C. Clementi, The Journal of Chemical Physics (2018).
LibreCat: /record/21943
DOI: 10.1063/1.5053582

Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias
F. Nüske, H. Wu, J.-H. Prinz, C. Wehmeyer, C. Clementi, F. Noé, The Journal of Chemical Physics (2017).
LibreCat: /record/21938
DOI: 10.1063/1.4976518

Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations
H. Wu, F. Nüske, F. Paul, S. Klus, P. Koltai, F. Noé, The Journal of Chemical Physics (2017).
LibreCat: /record/21939
DOI: 10.1063/1.4979344

Variational tensor approach for approximating the rare-event kinetics of macromolecular systems
F. Nüske, R. Schneider, F. Vitalini, F. Noé, The Journal of Chemical Physics (2016).
LibreCat: /record/21937
DOI: 10.1063/1.4940774

Variational Approach to Molecular Kinetics
F. Nüske, B.G. Keller, G. Pérez-Hernández, A.S.J.S. Mey, F. Noé, Journal of Chemical Theory and Computation (2014) 1739–1752.
LibreCat: /record/21936
DOI: 10.1021/ct4009156

A Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems
F. Noé, F. Nüske, Multiscale Modeling &; Simulation (2013) 635–655.
LibreCat: /record/21935
DOI: 10.1137/110858616