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Forschungsschwerpunkte
- (Nicht-lineare) Modellreduktion
- Reduzierte Basis Methoden
- Theorie und Anwendungen Neuronaler Netze
Vita
Akademische Ausbildung
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- MSc. Mathematik mit Nebenfach Informatik, Westfälische Wilhelms-Universität Münster
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- BSc. Mathematik mit Nebenfach Informatik, Rheinische Friedrich-Wilhelms-Universität Bonn
Beruflicher Werdegang
- seit
- Wissenschaftlicher Mitarbeiter, Arbeitsgruppe Ohlberger, Westfälische Wilhelms-Universität Münster
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- Studentische Hilfskraft, Institut für Analysis und Numerik, Westfälische Wilhelms-Universität Münster
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- Studentische Hilfskraft, Arithmeum, Rheinische Friedrich-Wilhelms-Universität Bonn
Publikationen
- . . ‘A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs.’ arXiv [math.NA] 2204.13454. doi: 10.48550/arXiv.2204.13454. [eingereicht / in Begutachtung]
- . . ‘Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain.’ ARGESIM Reports 17. doi: 10.11128/arep.17.a17129.
- . . ‘Adaptive machine learning based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery.’ Advances in Computational Mathematics 2022. [akzeptiert / in Druck (unveröffentlicht)]
Vorträge
Praxisorientierte Vorträge
- Kleikamp, Hendrik (): ‘Model order reduction using artificial neural networks’. pyMOR School 2022, Magdeburg (online), .
Wissenschaftliche Vorträge
- Kleikamp, Hendrik (): ‘Nonlinear model order reduction for hyperbolic conservation laws by means of diffeomorphic transformations of space-time domains’. Model Reduction and Surrogate Modeling (MORE), Berlin, .
- Kleikamp, Hendrik (): ‘Nonlinear model order reduction for parametrized hyperbolic conservation laws in a space-time domain’. Minisymposium on “Numerical methods for wave propagation problems” at CMAM (Computational Methods in Applied Mathematics) 2022, Wien, .
- Kleikamp, Hendrik (): ‘Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain’. Minisymposium on "Recent Advances in Model Reduction and Surrogate Modeling" at MATHMOD (International Conference on Mathematical Modelling) 2022, Wien, .
- Kleikamp, Hendrik (): ‘A certified and adaptive RB-ML-ROM surrogate approach for parametrized PDEs’. YMMOR - Young Mathematicians in Model Order Reduction, Münster, .
- Pelling, Art; Kleikamp, Hendrik (): ‘Introduction to System Theory and Model Order Reduction’. YMMOR - Young Mathematicians in Model Order Reduction, Münster, .
- Kleikamp, Hendrik (): ‘A certified and adaptive RB-ML-ROM surrogate approach for parametrized PDEs’. HCM Workshop: Synergies between Data Science and PDE Analysis (Hausdorff Center for Mathematics), Bonn, .
- Kleikamp, Hendrik (): ‘Nonlinear Model Order Reduction Using Geodesic Shooting in the Diffeomorphism Group’. Minisymposium on "Nonlinear Model Reduction Methods for Random or Parametric Time Dependent Problems" at SIAM UQ (SIAM Conference on Uncertainty Quantification) 2022 (SIAM - Society for Industrial and Applied Mathematics), Atlanta, Georgia (hybrid), .
- Kleikamp, Hendrik (): ‘Model order reduction using artificial neural networks’. pyMOR School 2021, Münster, .