On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation
K. Bieker, B. Gebken, S. Peitz, IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2022) 7797–7808.
LibreCat: /record/20731
DOI: 10.1109/TPAMI.2021.3114962

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

Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults
C. Goelz, K. Mora, J.K. Stroehlein, F.K. Haase, M. Dellnitz, C. Reinsberger, S. Vieluf, Cognitive Neurodynamics (2021).
LibreCat: /record/21195
DOI: 10.1007/s11571-020-09656-9

Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models
M.B. Berkemeier, S. Peitz, Mathematical and Computational Applications 26 (2021).
LibreCat: /record/21337
DOI: 10.3390/mca26020031

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

An efficient descent method for locally Lipschitz multiobjective optimization problems
B. Gebken, S. Peitz, Journal of Optimization Theory and Applications 188 (2021) 696–723.
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

Inverse multiobjective optimization: Inferring decision criteria from data
B. Gebken, S. Peitz, Journal of Global Optimization 80 (2021) 3–29.
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

The Computation and Analysis of Invariant Sets of Infinite-Dimensional Systems
R. Gerlach, The Computation and Analysis of Invariant Sets of Infinite-Dimensional Systems, 2021.
LibreCat: /record/32057
DOI: 10.17619/UNIPB/1-1278

Explicit multiobjective model predictive control for nonlinear systems  with symmetries
S. Ober-Blöbaum, S. Peitz, International Journal of Robust and Nonlinear Control 31(2) (2021) 380–403.
LibreCat: /record/16294
DOI: 10.1002/rnc.5281

Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach
K. Flaßkamp, S. Ober-Blöbaum, S. Peitz, in: O. Junge, O. Schütze, G. Froyland, S. Ober-Blöbaum, K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation, Springer, Cham, 2020.
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

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

Pareto Explorer: a global/local exploration tool for many-objective optimization problems
O. Schütze, O. Cuate, A. Martín, S. Peitz, M. Dellnitz, Engineering Optimization 52 (2020) 832–855.
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

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

Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator
S. Peitz, S. Klus, in: Lecture Notes in Control and Information Sciences, Springer, Cham, 2020, pp. 257–282.
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

Deep model predictive flow control with limited sensor data and online learning
K. Bieker, S. Peitz, S.L. Brunton, J.N. Kutz, M. Dellnitz, Theoretical and Computational Fluid Dynamics 34 (2020) 577–591.
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

Data-Driven Model Predictive Control using Interpolated Koopman  Generators
S. Peitz, S.E. Otto, C.W. Rowley, SIAM Journal on Applied Dynamical Systems 19 (2020) 2162–2193.
LibreCat: /record/16309
DOI: 10.1137/20M1325678

A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors
R. Gerlach, A. Ziessler, B. Eckhardt, M. Dellnitz, SIAM Journal on Applied Dynamical Systems (2020) 705–723.
LibreCat: /record/16710
DOI: 10.1137/19m1247139

Explicit Multi-objective Model Predictive Control for Nonlinear Systems  Under Uncertainty
C.I. Hernández Castellanos, S. Ober-Blöbaum, S. Peitz, International Journal of Robust and Nonlinear Control 30(17) (2020) 7593–7618.
LibreCat: /record/16297
DOI: 10.1002/rnc.5197

The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems
R. Gerlach, A. Ziessler, in: O. Junge, O. Schütze, S. Ober-Blöbaum, K. Padberg-Gehle (Eds.), Advances in Dynamics, Optimization and Computation, Springer International Publishing, Cham, 2020, pp. 66–85.
LibreCat: /record/17994
DOI: 10.1007/978-3-030-51264-4_3