Hendrik Kleikamp, MSc
Professur für Angewandte Mathematik, insbesondere Numerik (Prof. Ohlberger)
Forschungsschwerpunkte
- (Nicht-lineare) Modellreduktion
- Reduzierte Basis Methoden
- Theorie und Anwendungen Neuronaler Netze
- (Optimale) Steuerung dynamischer Systeme
Akademische Ausbildung
- MSc. Mathematik mit Nebenfach Informatik, Westfälische Wilhelms-Universität Münster
- BSc. Mathematik mit Nebenfach Informatik, Rheinische Friedrich-Wilhelms-Universität Bonn
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Lehre
- Vorlesung: Numerik Partieller Differentialgleichungen II [106353]
(zusammen mit Julia Schleuß, Prof. Dr. Mario Ohlberger)
[ - | | wöchentlich | Mo. | SRZ 203 | Julia Schleuß]
[ - | | wöchentlich | Do. | SRZ 203 | Julia Schleuß]
- Übung: Übungen Numerik partieller Differentialgleichungen [104496]
(zusammen mit Prof. Dr. Mario Ohlberger) - Vorlesung: Numerik partieller Differentialgleichungen [104495]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Übung: Übungen zur Vorlesung Angewandte Funktionalanalysis [102400]
(zusammen mit Julia Schleuß, Prof. Dr. Mario Ohlberger) - Vorlesung: Angewandte Funktionalanalysis [102399]
(zusammen mit Julia Schleuß, Prof. Dr. Mario Ohlberger)
- Übung: Übungen zur Vorlesung: Analysis & Numerik von Differentialgleichungen [100388]
(zusammen mit Prof. Dr. Mario Ohlberger) - Praktikum: Einführung in die Numerik mit Python [100378]
(zusammen mit Lukas Renelt, Prof. Dr. Mario Ohlberger) - Vorlesung: Analysis und Numerik von Differentialgleichungen [100418]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Übung: Exercises to Numerical Optimization [108409]
(zusammen mit Prof. Dr. Mario Ohlberger) - Vorlesung: Numerical Optimization [108408]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Praktikum: Nichtlineare Modellierung in den Naturwissenschaften [106351]
(zusammen mit Julia Schleuß, Prof. Dr. Christian Engwer, Priv.-Doz. Svetlana Gurevich, Lukas Renelt, Prof. Dr. Mario Ohlberger, Prof. Dr. André Schlichting, Prof. Dr. Andreas Heuer) - Vorlesung: Numerik partieller Differentialgleichungen [106352]
(zusammen mit Prof. Dr. Christian Engwer)
- Übung: Übungen zu Numerik Partieller Differentialgleichungen II [104383]
(zusammen mit Prof. Dr. Mario Ohlberger)
- Übung: Übungen zu Inversen Problemen [102375]
(zusammen mit Dr. Frank Wübbeling)
- Vorlesung: Numerik Partieller Differentialgleichungen II [106353]
Publikationen
- . . Application of an adaptive model hierarchy to parametrized optimal control problems arXiv. 1. Aufl. . doi: 10.48550/arXiv.2402.10708. [submitted / under review]
- . . ‘Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines.’ In Communications in Computer and Information Science.: Springer. [accepted / in Press (not yet published)]
- . . Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems arXiv. doi: 10.48550/arXiv.2307.15590. [submitted / under review]
- . . ‘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.
- . . Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling arXiv. doi: 10.48550/ARXIV.2302.14526. [accepted / in Press (not yet published)]
- . . ‘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.
- . . ‘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.
Vorträge
- 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 (Slovak University of Technology in Bratislava, Comenius University in Bratislava, Algoritmy:SK s.r.o. and Union of the Slovak Mathematicians and Physicists), Podbanske, .
- Kleikamp, Hendrik (): ‘Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems’. YMMOR - Young Mathematicians in Model Order Reduction, Stuttgart, .
Wissenschaftliche Vorträge
- 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, .
Praxisorientierte Vorträge
- 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 (SIAM - Society for Industrial and Applied Mathematics), Amsterdam, .
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), .
Praxisorientierte Vorträge
- 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, .