Bieker, Katharina, et al. “On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation.”10.1109/TPAMI.2021.3114962__IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, IEEE, 2022, pp. 7797–808, doi:10.1109/TPAMI.2021.3114962.
LibreCat: /record/20731
DOI: 10.1109/TPAMI.2021.3114962

Nüske, Feliks, et al. “Tensor-Based Computation of Metastable and Coherent Sets.”10.1016/j.physd.2021.133018__Physica D: Nonlinear Phenomena, 133018, 2021, doi:10.1016/j.physd.2021.133018.
LibreCat: /record/24169
DOI: 10.1016/j.physd.2021.133018

Klus, Stefan, et al. “Symmetric and Antisymmetric Kernels for Machine Learning Problems in Quantum Physics and Chemistry.”10.1088/2632-2153/ac14ad__Machine Learning: Science and Technology, 045016, 2021, doi:10.1088/2632-2153/ac14ad.
LibreCat: /record/24170
DOI: 10.1088/2632-2153/ac14ad

Goelz, Christian, et al. “Electrophysiological Signatures of Dedifferentiation Differ between Fit and Less Fit Older Adults.”10.1007/s11571-020-09656-9__Cognitive Neurodynamics, 2021, doi:10.1007/s11571-020-09656-9.
LibreCat: /record/21195
DOI: 10.1007/s11571-020-09656-9

Berkemeier, Manuel Bastian, and Sebastian Peitz. “Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models.”10.3390/mca26020031__Mathematical and Computational Applications, vol. 26, no. 2, 31, 2021, doi:10.3390/mca26020031.
LibreCat: /record/21337
DOI: 10.3390/mca26020031

Nüske, Feliks, et al. “Spectral Properties of Effective Dynamics from Conditional Expectations.”10.3390/e23020134__Entropy, 134, 2021, doi:10.3390/e23020134.
LibreCat: /record/21820
DOI: 10.3390/e23020134

Gebken, Bennet, and Sebastian Peitz. “An Efficient Descent Method for Locally Lipschitz Multiobjective Optimization Problems.”10.1007/s10957-020-01803-w__Journal of Optimization Theory and Applications, vol. 188, 2021, pp. 696–723, doi:10.1007/s10957-020-01803-w.
LibreCat: /record/16867
DOI: 10.1007/s10957-020-01803-w

Gebken, Bennet, and Sebastian Peitz. “Inverse Multiobjective Optimization: Inferring Decision Criteria from Data.”10.1007/s10898-020-00983-z__Journal of Global Optimization, vol. 80, Springer, 2021, pp. 3–29, doi:10.1007/s10898-020-00983-z.
LibreCat: /record/16295
DOI: 10.1007/s10898-020-00983-z

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

Ober-Blöbaum, Sina, and Sebastian Peitz. “Explicit Multiobjective Model Predictive Control for Nonlinear Systems  with Symmetries.”10.1002/rnc.5281__International Journal of Robust and Nonlinear Control, vol. 31(2), 2021, pp. 380–403, doi:10.1002/rnc.5281.
LibreCat: /record/16294
DOI: 10.1002/rnc.5281

Flaßkamp, Kathrin, et al. “Symmetry in Optimal Control: A Multiobjective Model Predictive Control Approach.”10.1007/978-3-030-51264-4_9__Advances in Dynamics, Optimization and Computation, edited by Oliver Junge et al., Springer, 2020, doi:10.1007/978-3-030-51264-4_9.
LibreCat: /record/17411
DOI: 10.1007/978-3-030-51264-4_9

Klus, Stefan, et al. “Kernel-Based Approximation of the Koopman Generator and Schrödinger Operator.”10.3390/e22070722__Entropy, 722, 2020, doi:10.3390/e22070722.
LibreCat: /record/21819
DOI: 10.3390/e22070722

Schütze, Oliver, et al. “Pareto Explorer: A Global/Local Exploration Tool for Many-Objective Optimization Problems.”10.1080/0305215x.2019.1617286__Engineering Optimization, vol. 52, no. 5, 2020, pp. 832–55, doi:10.1080/0305215x.2019.1617286.
LibreCat: /record/10596
DOI: 10.1080/0305215x.2019.1617286

Klus, Stefan, et al. “Data-Driven Approximation of the Koopman Generator: Model Reduction, System Identification, and Control.”10.1016/j.physd.2020.132416__Physica D: Nonlinear Phenomena, vol. 406, 132416, 2020, doi:10.1016/j.physd.2020.132416.
LibreCat: /record/16288
DOI: 10.1016/j.physd.2020.132416

Peitz, Sebastian, and Stefan Klus. “Feedback Control of Nonlinear PDEs Using Data-Efficient Reduced Order Models Based on the Koopman Operator.”10.1007/978-3-030-35713-9_10__Lecture Notes in Control and Information Sciences, vol. 484, Springer, 2020, pp. 257–82, doi:10.1007/978-3-030-35713-9_10.
LibreCat: /record/16289
DOI: 10.1007/978-3-030-35713-9_10

Bieker, Katharina, et al. “Deep Model Predictive Flow Control with Limited Sensor Data and Online Learning.”10.1007/s00162-020-00520-4__Theoretical and Computational Fluid Dynamics, vol. 34, 2020, pp. 577–591, doi:10.1007/s00162-020-00520-4.
LibreCat: /record/16290
DOI: 10.1007/s00162-020-00520-4

Peitz, Sebastian, et al. “Data-Driven Model Predictive Control Using Interpolated Koopman  Generators.”10.1137/20M1325678__SIAM Journal on Applied Dynamical Systems, vol. 19, no. 3, 2020, pp. 2162–93, doi:10.1137/20M1325678.
LibreCat: /record/16309
DOI: 10.1137/20M1325678

Gerlach, Raphael, et al. “A Set-Oriented Path Following Method for the Approximation of Parameter Dependent Attractors.”10.1137/19m1247139__SIAM Journal on Applied Dynamical Systems, 2020, pp. 705–23, doi:10.1137/19m1247139.
LibreCat: /record/16710
DOI: 10.1137/19m1247139

Hernández Castellanos, Carlos Ignacio, et al. “Explicit Multi-Objective Model Predictive Control for Nonlinear Systems  Under Uncertainty.”10.1002/rnc.5197__International Journal of Robust and Nonlinear Control, vol. 30(17), 2020, pp. 7593–618, doi:10.1002/rnc.5197.
LibreCat: /record/16297
DOI: 10.1002/rnc.5197

Gerlach, Raphael, and Adrian Ziessler. “The Approximation of Invariant Sets in Infinite Dimensional Dynamical Systems.”10.1007/978-3-030-51264-4_3__Advances in Dynamics, Optimization and Computation, edited by Oliver Junge et al., vol. 304, Springer International Publishing, 2020, pp. 66–85, doi:10.1007/978-3-030-51264-4_3.
LibreCat: /record/17994
DOI: 10.1007/978-3-030-51264-4_3