| Research Interests | (Nonlinear) Model order reduction Reduced basis methods Theory and applications of neural networks |
| Current Talks | • Model order reduction with artificial neural networks in pyMOR. Minitutorial on "pyMOR - Model Order Reduction with Python" at SIAM CSE (SIAM Conference on Computational Science and Engineering) 2023, Amsterdam Slides Link to event • Nonlinear model order reduction for hyperbolic conservation laws by means of diffeomorphic transformations of space-time domains. Model Reduction and Surrogate Modeling (MORE), Berlin Slides Link to event • 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 Slides Link to event • Model order reduction using artificial neural networks. pyMOR School 2022, Magdeburg (online) Slides Link to event • 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 Slides Link to event • A certified and adaptive RB-ML-ROM surrogate approach for parametrized PDEs. YMMOR - Young Mathematicians in Model Order Reduction, Münster Slides Link to event • Introduction to System Theory and Model Order Reduction. YMMOR - Young Mathematicians in Model Order Reduction, Münster Slides Link to event • A certified and adaptive RB-ML-ROM surrogate approach for parametrized PDEs. HCM Workshop: Synergies between Data Science and PDE Analysis, Bonn Slides Link to event • 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, Atlanta, Georgia (hybrid) Slides Link to event |
| Current Publications | • Wenzel, Tizian; Haasdonk, Bernard; Kleikamp, Hendrik; Ohlberger, Mario; Schindler, Felix Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. , 2023 online • Haasdonk B, Kleikamp H, Ohlberger M, Schindler F, Wenzel T A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs. SIAM Journal on Scientific Computing Vol. 2022, 2022 online • Kleikamp Hendrik, Ohlberger Mario, Rave Stephan Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain. ARGESIM Reports Vol. 17, 2022 online • Keil T, Kleikamp H, Lorentzen R, Oguntola M, Ohlberger M Adaptive machine learning based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery. Advances in Computational Mathematics Vol. 2022 (48), 2022 online | hendrik dot kleikamp at uni-muenster dot de |
| Phone | +49 251 83-35060 |
| FAX | +49 251 83-32729 |
| Room | 120.007 |
| Secretary | Sekretariat Wernke Frau Silvia Wernke Telefon +49 251 83-35052 Fax +49 251 83-32729 Zimmer 120.001 |
| Address | Herr Hendrik Kleikamp Angewandte Mathematik Münster: Institut für Analysis und Numerik Fachbereich Mathematik und Informatik der Universität Münster Orléans-Ring 10 48149 Münster |
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