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282 Publications


2021 | Preprint | LibreCat-ID: 21600
Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical integration using reinforcement  learning. arXiv:210403562. 2021.
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2021 | Journal Article | LibreCat-ID: 16295
Gebken B, Peitz S. Inverse multiobjective optimization: Inferring decision criteria from data. Journal of Global Optimization. 2021;80:3-29. doi:10.1007/s10898-020-00983-z
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2021 | Preprint | LibreCat-ID: 23428
Nüske F, Peitz S, Philipp F, Schaller M, Worthmann K. Finite-data error bounds for Koopman-based prediction and control. arXiv:210807102. 2021.
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2021 | Preprint | LibreCat-ID: 21199
Peitz S, Bieker K. On the Universal Transformation of Data-Driven Models to Control Systems. arXiv:210204722. 2021.
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2021 | Conference Paper | LibreCat-ID: 22894
Offen C, Ober-Blöbaum S. Bifurcation preserving discretisations of optimal control problems.
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2021 | Journal Article | LibreCat-ID: 16867
Gebken B, Peitz S. An efficient descent method for locally Lipschitz multiobjective optimization problems. Journal of Optimization Theory and Applications. 2021;188:696-723. doi:10.1007/s10957-020-01803-w
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2021 | Journal Article | LibreCat-ID: 21195
Goelz C, Mora K, Stroehlein JK, et al. Electrophysiological signatures of dedifferentiation differ between fit and less fit older adults. Cognitive Neurodynamics. 2021. doi:10.1007/s11571-020-09656-9
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2021 | Journal Article | LibreCat-ID: 24170
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. Published online 2021. doi:10.1088/2632-2153/ac14ad
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2021 | Journal Article | LibreCat-ID: 20731
Bieker K, Gebken B, Peitz S. On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation. IEEE Transactions on Pattern Analysis and Machine Intelligence. Published online 2021. doi:10.1109/TPAMI.2021.3114962
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2021 | Preprint | LibreCat-ID: 23382
Offen C, Ober-Blöbaum S. Symplectic integration of learned Hamiltonian systems.
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2021 | Journal Article | LibreCat-ID: 24169
Nüske F, Gelß P, Klus S, Clementi C. Tensor-based computation of metastable and coherent sets. Physica D: Nonlinear Phenomena. Published online 2021. doi:10.1016/j.physd.2021.133018
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2021 | Journal Article | LibreCat-ID: 16294
Ober-Blöbaum S, Peitz S. Explicit multiobjective model predictive control for nonlinear systems  with symmetries. International Journal of Robust and Nonlinear Control. 2021;31(2):380-403. doi:10.1002/rnc.5281
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2021 | Preprint | LibreCat-ID: 21572
Ridderbusch S, Offen C, Ober-Blöbaum S, Goulart P. Learning ODE Models with Qualitative Structure Using Gaussian Processes .
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2021 | Journal Article | LibreCat-ID: 21337
Berkemeier MB, Peitz S. Derivative-Free Multiobjective Trust Region Descent Method Using Radial  Basis Function Surrogate Models. Mathematical and Computational Applications. 2021;26(2). doi:10.3390/mca26020031
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2021 | Journal Article | LibreCat-ID: 21820
Nüske F, Koltai P, Boninsegna L, Clementi C. Spectral Properties of Effective Dynamics from Conditional Expectations. Entropy. 2021. doi:10.3390/e23020134
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2020 | Journal Article | LibreCat-ID: 16961
Liebendörfer M, Göller R, Biehler R, et al. LimSt – Ein Fragebogen zur Erhebung von Lernstrategien im mathematikhaltigen Studium. Journal für Mathematik-Didaktik. 2020. doi:10.1007/s13138-020-00167-y
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2020 | Journal Article | LibreCat-ID: 16290
Bieker K, Peitz S, Brunton SL, Kutz JN, Dellnitz M. Deep model predictive flow control with limited sensor data and online learning. Theoretical and Computational Fluid Dynamics. 2020;34:577–591. doi:10.1007/s00162-020-00520-4
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2020 | Preprint | LibreCat-ID: 19941
McLachlan RI, Offen C. Backward error analysis for variational discretisations of partial  differential equations. arXiv:200614172.
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2020 | Journal Article | LibreCat-ID: 19939
Kreusser LM, McLachlan RI, Offen C. Detection of high codimensional bifurcations in variational PDEs. Nonlinearity. 2020;33(5):2335-2363. doi:10.1088/1361-6544/ab7293
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2020 | Journal Article | LibreCat-ID: 16288
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. 2020;406. doi:10.1016/j.physd.2020.132416
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