Hendrik Kleikamp, MSc
Professorship of Applied Mathematics, especially Numerics (Prof. Ohlberger)
Research Foci
- Nonlinear model order reduction for transport-dominated problems
- Reduced basis methods and linear model order reduction
- Scientific machine learning (neural networks, kernel methods)
- (Optimal) Control of dynamical systems
CV
Academic Education
- MSc. Mathematics with Minor in Computer Science, Westfälische Wilhelms-Universität Münster
- BSc. Mathematics with Minor in Computer Science, Rheinische Friedrich-Wilhelms-Universität Bonn
Positions
- Scientific Assistant, Workgroup Ohlberger, Westfälische Wilhelms-Universität Münster
- Student Assistant, Institute for Analysis and Numerics, Westfälische Wilhelms-Universität Münster
- Student Assistant, Arithmeum, Rheinische Friedrich-Wilhelms-Universität Bonn
Teaching
- Vorlesung: Model Order Reduction [108361]
(in cooperation with Dr. Stephan Rave)
[ - | | wöchentlich | Di | M B 6 (M 6) | Hendrik Kleikamp]
[ - | | wöchentlich | Fr. | M B 6 (M 6) | Hendrik Kleikamp] - Tutorial Model Order Reduction [108410]
(in cooperation with Dr. Stephan Rave)
[ - | | wöchentlich | Mo | M A 401 (SR 4) | Hendrik Kleikamp]
- Vorlesung: Scientific Computing [106353]
(in cooperation with Julia Schleuß, Prof. Dr. Mario Ohlberger)
- Vorlesung: Numerics for partial differential equations [104495]
(in cooperation with Prof. Dr. Mario Ohlberger) - Tutorial Numerics of Partial Differential Equations [104496]
(in cooperation with Prof. Dr. Mario Ohlberger)
- Vorlesung: Applied Functional Analysis [102399]
(in cooperation with Julia Schleuß, Prof. Dr. Mario Ohlberger) - Tutorial Applied Functional Analysis [102400]
(in cooperation with Julia Schleuß, Prof. Dr. Mario Ohlberger)
- Vorlesung: Analysis & Numeric of Differential Equations [100418]
(in cooperation with Prof. Dr. Mario Ohlberger) - Praktikum: Introduction to Numerical Programming with Python [100378]
(in cooperation with Lukas Renelt, Prof. Dr. Mario Ohlberger) - Tutorial Analysis & Numeric of Differential Equations [100388]
(in cooperation with Prof. Dr. Mario Ohlberger)
- Vorlesung: Numerical Optimization [108408]
(in cooperation with Prof. Dr. Mario Ohlberger) - Tutorial Numerical Optimization [108409]
(in cooperation with Prof. Dr. Mario Ohlberger)
- Vorlesung: Numerics for partial differential equations [106352]
(in cooperation with Prof. Dr. Christian Engwer) - Praktikum: Non-linear modelling in the natural sciences [106351]
(in cooperation with Julia Schleuß, Prof. Dr. Christian Engwer, apl. Prof. Svetlana Gurevich, Lukas Renelt, Prof. Dr. Mario Ohlberger, Prof. Dr. André Schlichting, Prof. Dr. Andreas Heuer)
- Tutorial Numerical Analysis of Partial Differential Equations II [104383]
(in cooperation with Prof. Dr. Mario Ohlberger)
- Tutorial Inverse Problems [102375]
(in cooperation with Dr. Frank Wübbeling)
- Vorlesung: Model Order Reduction [108361]
Publications
- Wenzel, Tizian, Haasdonk, Bernard, Kleikamp, Hendrik, Ohlberger, Mario, and Schindler, Felix. . “Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling.” in Large-Scale Scientific Computations, Vol. 13952 of Lecture Notes in Computer Science, edited by I. Lirkov and S. Margenov. Berlin: Springer Nature. doi: 10.1007/978-3-031-56208-2_11.
- Schembera, Björn, Wübbeling, Frank, Kleikamp, Hendrik, Biedinger, Christine, Fiedler, Jochen, Reidelbach, Marco, Shehu, Aurela, Schmidt, Burkhard, Koprucki, Thomas, Iglezakis, Dorothea, and Göddeke, Dominik. . “Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines.” in Metadata and Semantic Research - 17th Research Conference, MTSR 2023, Milan, Italy, October 25–27, 2023, Revised Selected Papers, Vol. 2048 of Communications in Computer and Information Science, edited by Emmanouel Garoufallou and Fabio Sartori. Heidelberg: Springer. doi: 10.1007/978-3-031-65990-4_14.
- Schembera, Björn, Wübbeling, Frank, Kleikamp, Hendrik, Schmidt, Burkhard, Shehu, Aurela, Reidelbach, Marco, Biedinger, Christine, Fiedler, Jochen, Koprucki, Thomas, Iglezakis, Dorothea, and Göddeke, Dominik. Forthcoming. “Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics.” in Proceedings of the 18th International Conference on Metadata and Semantics Research 2024 Heidelberg: Springer.
- Kleikamp, Hendrik, and Ohlberger, Mario. . “Adaptive Model Hierarchies for Multi-Query Scenarios.” arXiv doi: 10.48550/arXiv.2411.17252.
- Kleikamp, Hendrik, and Wenzel, Tizian. . “Kernel Methods in the Deep Ritz framework: Theory and practice.” arXiv doi: 10.48550/arXiv.2410.03503.
- Kleikamp, Hendrik, and Renelt, Lukas. . “Two-stage model reduction approaches for the efficient and certified solution of parametrized optimal control problems.” arXiv doi: 10.48550/arXiv.2408.15900.
- Kleikamp, Hendrik. . “Application of an adaptive model hierarchy to parametrized optimal control problems.” in
- Kleikamp, Hendrik, Lazar, Martin, and Molinari, Cesare. Forthcoming. “Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems.” arXiv doi: 10.48550/arXiv.2307.15590.
- Haasdonk, B, Kleikamp, H, Ohlberger, M, Schindler, F, and Wenzel, T. . “A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs.” SIAM Journal on Scientific Computing, № 45 (3): A1039–1065. doi: 10.1137/22M1493318.
- Kleikamp, Hendrik, Ohlberger, Mario, and Rave, Stephan. . “Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain.” in MATHMOD 2022 - Discussion Contribution Volume, Vol. 17 of ARGESIM Report, edited by Felix Breitenecker, Wolfgang Kemmetmüller, Andreas Körner, Andreas Kugi and Inge Troch. Wien: ARGESIM Verlag. doi: 10.11128/arep.17.a17129.
- Keil, T, Kleikamp, H, Lorentzen, R, Oguntola, M, and Ohlberger, M. . “Adaptive machine learning based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery.” Advances in Computational Mathematics, № 2022 (48) 73. doi: 10.1007/s10444-022-09981-z.
Talks
scientific talks
- Kleikamp, Hendrik : “Certified machine learning for model order reduction of parametrized problems”. Ulysseus Research Workshop on Mathematics in Machine Learning, Online, .
- Kleikamp, Hendrik : “Adaptive model hierarchies for solving parametrized optimal control problems in multi-query scenarios”. Seminar Talk, Genova, .
- Kleikamp, Hendrik : “Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems”. Minisymposium on "New Trends in Model Order Reduction and Learning" at ALGORITMY 2024, Central-European Conference on Scientific Computing, Podbanske, .
- Kleikamp, Hendrik : “Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems”. YMMOR - Young Mathematicians in Model Order Reduction, Stuttgart, .
practical talks
- Kleikamp, Hendrik : “Reduced Basis Methods with pyMOR”. pyMOR School and User Meeting 2024, Münster, .
scientific talks
- Kleikamp, Hendrik : “Be greedy and learn - efficient and certified algorithms for parametrized optimal control problems”. Oberseminar Numerik, Münster, .
- Kleikamp, Hendrik : “A new Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs”. Minisymposium on "Adaptive Methods for Surrogate and Reduced Order Modeling" at ADMOS (XI International Conference on Adaptive Modeling and Simulation), Göteborg, .
- Kleikamp, Hendrik : “Nonlinear model order reduction for parametrized transport-dominated PDEs using registration-based methods”. YMMOR - Young Mathematicians in Model Order Reduction, Ulm, .
practical talks
- Kleikamp, Hendrik : “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, .
scientific talks
- 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, 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, Atlanta, Georgia (hybrid), .
practical talks
- Kleikamp, Hendrik : “Model order reduction using artificial neural networks”. pyMOR School 2022, Magdeburg (online), .
- Kleikamp, Hendrik : “Model order reduction using artificial neural networks”. pyMOR School 2021, Münster, .