Hendrik Kleikamp, MSc
Professur für Angewandte Mathematik, insbesondere Numerik (Prof. Ohlberger)
Forschungsschwerpunkte
- Nicht-lineare Modellreduktion für transport-dominierte Probleme
- Reduzierte Basis Methoden und lineare Modellreduktion
- Wissenschaftliches maschinelles Lernen (neuronale Netze, Kernelmethoden)
- (Optimale) Steuerung dynamischer Systeme
Vita
Akademische Ausbildung
- MSc. Mathematik mit Nebenfach Informatik, Westfälische Wilhelms-Universität Münster
- BSc. Mathematik mit Nebenfach Informatik, Rheinische Friedrich-Wilhelms-Universität Bonn
Beruflicher Werdegang
- Wissenschaftlicher Mitarbeiter, Arbeitsgruppe Ohlberger, Westfälische Wilhelms-Universität Münster
- Studentische Hilfskraft, Institut für Analysis und Numerik, Westfälische Wilhelms-Universität Münster
- Studentische Hilfskraft, Arithmeum, Rheinische Friedrich-Wilhelms-Universität Bonn
Lehre
- Vorlesung: Model Order Reduction [108361]
(zusammen mit Dr. Stephan Rave)
[ - | | wöchentlich | Di | M B 6 (M 6) | Hendrik Kleikamp]
[ - | | wöchentlich | Fr | M B 6 (M 6) | Hendrik Kleikamp] - Übungen zu Model Order Reduction [108410]
(zusammen mit Dr. Stephan Rave)
[ - | | wöchentlich | Mo | M A 401 (SR 4) | Hendrik Kleikamp]
- Vorlesung: Numerik Partieller Differentialgleichungen II [106353]
(zusammen mit Julia Schleuß, Prof. Dr. Mario Ohlberger)
- Vorlesung: Numerik partieller Differentialgleichungen [104495]
(zusammen mit Prof. Dr. Mario Ohlberger) - Übungen Numerik partieller Differentialgleichungen [104496]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Vorlesung: Angewandte Funktionalanalysis [102399]
(zusammen mit Julia Schleuß, Prof. Dr. Mario Ohlberger) - Übungen zur Vorlesung Angewandte Funktionalanalysis [102400]
(zusammen mit Julia Schleuß, Prof. Dr. Mario Ohlberger)
- Vorlesung: Analysis und Numerik von Differentialgleichungen [100418]
(zusammen mit Prof. Dr. Mario Ohlberger) - Praktikum: Einführung in die Numerik mit Python [100378]
(zusammen mit Lukas Renelt, Prof. Dr. Mario Ohlberger) - Übungen zur Vorlesung: Analysis & Numerik von Differentialgleichungen [100388]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Vorlesung: Numerical Optimization [108408]
(zusammen mit Prof. Dr. Mario Ohlberger) - Exercises to Numerical Optimization [108409]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Vorlesung: Numerik partieller Differentialgleichungen [106352]
(zusammen mit Prof. Dr. Christian Engwer) - Praktikum: Nichtlineare Modellierung in den Naturwissenschaften [106351]
(zusammen mit Julia Schleuß, Prof. Dr. Christian Engwer, apl. Prof. Svetlana Gurevich, Lukas Renelt, Prof. Dr. Mario Ohlberger, Prof. Dr. André Schlichting, Prof. Dr. Andreas Heuer)
- Übungen zu Numerik Partieller Differentialgleichungen II [104383]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Übungen zu Inversen Problemen [102375]
(zusammen mit Dr. Frank Wübbeling)
- Vorlesung: Model Order Reduction [108361]
Publikationen
- . . ‘Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling.’ In Large-Scale Scientific Computations, edited by , 117–125. Berlin: Springer Nature. doi: 10.1007/978-3-031-56208-2_11.
- . . ‘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, edited by , 161–168. Heidelberg: Springer. doi: 10.1007/978-3-031-65990-4_14.
- . . ‘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. [accepted / in Press (not yet published)]
- . Kernel Methods in the Deep Ritz framework: Theory and practice arXiv. doi: 10.48550/arXiv.2410.03503. [submitted / under review]
- . Two-stage model reduction approaches for the efficient and certified solution of parametrized optimal control problems arXiv. doi: 10.48550/arXiv.2408.15900. [submitted / under review]
- . . ‘Application of an adaptive model hierarchy to parametrized optimal control problems.’ Contributed to the Algoritmy, Podbanske.
- . Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems arXiv. doi: 10.48550/arXiv.2307.15590. [accepted / in Press (not yet published)]
- . . ‘A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs.’ SIAM Journal on Scientific Computing 45, Nr. 3: A1039. doi: 10.1137/22M1493318.
- . . ‘Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain.’ In MATHMOD 2022 - Discussion Contribution Volume, edited by , 57–58. Wien: ARGESIM Verlag. 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, Nr. 48: 73. doi: 10.1007/s10444-022-09981-z.
Vorträge
practical talks
- Kleikamp, Hendrik : “Reduced Basis Methods with pyMOR”. pyMOR School and User Meeting 2024, Münster, .
scientific talks
- 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 : “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 : “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 using artificial neural networks”. pyMOR School 2022, Magdeburg (online), .
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), .
- Kleikamp, Hendrik : “Model order reduction using artificial neural networks”. pyMOR School 2021, Münster, .