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Feliks Nüske
Feliks Nüske
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg
Bestätigte E-Mail-Adresse bei mpi-magdeburg.mpg.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
A variational approach to modeling slow processes in stochastic dynamical systems
F Noé, F Nuske
Multiscale Modeling & Simulation 11 (2), 635-655, 2013
2922013
Variational approach to molecular kinetics
F Nüske, BG Keller, G Pérez-Hernández, ASJS Mey, F Noé
Journal of chemical theory and computation 10 (4), 1739-1752, 2014
2832014
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 28, 985-1010, 2018
2612018
Sparse learning of stochastic dynamical equations
L Boninsegna, F Nüske, C Clementi
The Journal of chemical physics 148 (24), 2018
2042018
Data-driven approximation of the Koopman generator: Model reduction, system identification, and control
S Klus, F Nüske, S Peitz, JH Niemann, C Clementi, C Schütte
Physica D: Nonlinear Phenomena 406, 132416, 2020
1902020
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 146 (15), 2017
1162017
Markov state models from short non-equilibrium simulations—Analysis and correction of estimation bias
F Nüske, H Wu, JH Prinz, C Wehmeyer, C Clementi, F Noé
The Journal of Chemical Physics 146 (9), 2017
722017
Quantitative comparison of adaptive sampling methods for protein dynamics
E Hruska, JR Abella, F Nüske, LE Kavraki, C Clementi
The Journal of chemical physics 149 (24), 2018
572018
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 144 (5), 2016
522016
Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator
S Klus, F Nüske, B Hamzi
Entropy 22 (7), 722, 2020
352020
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 (1), 14, 2023
332023
Coarse-graining molecular systems by spectral matching
F Nüske, L Boninsegna, C Clementi
The Journal of chemical physics 151 (4), 2019
282019
Rapid calculation of molecular kinetics using compressed sensing
F Litzinger, L Boninsegna, H Wu, F Nüske, R Patel, R Baraniuk, F Noé, ...
Journal of chemical theory and computation 14 (5), 2771-2783, 2018
252018
Tensor-based computation of metastable and coherent sets
F Nüske, P Gelß, S Klus, C Clementi
Physica D: Nonlinear Phenomena 427, 133018, 2021
13*2021
Spectral properties of effective dynamics from conditional expectations
F Nüske, P Koltai, L Boninsegna, C Clementi
Entropy 23 (2), 134, 2021
132021
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 2 (4), 045016, 2021
122021
Towards reliable data-based optimal and predictive control using extended DMD
M Schaller, K Worthmann, F Philipp, S Peitz, F Nüske
IFAC-PapersOnLine 56 (1), 169-174, 2023
92023
Koopman analysis of quantum systems
S Klus, F Nüske, S Peitz
J. Phys. A: Math. Theor. 55, 314002, 2022
82022
Slicing and Dicing: Optimal Coarse-Grained Representation to Preserve Molecular Kinetics
W Yang, C Templeton, D Rosenberger, A Bittracher, F Nüske, F Noé, ...
ACS Central Science 9 (2), 186-196, 2023
62023
tgEDMD: Approximation of the Kolmogorov Operator in Tensor Train Format
M Lücke, F Nüske
Journal of Nonlinear Science 32 (4), 44, 2022
52022
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