Hendrik Kleikamp

According to our database, Hendrik Kleikamp authored at least 10 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Kernel Methods in the Deep Ritz framework: Theory and practice.

Hendrik Kleikamp, Tizian Wenzel

CoRR, 2024

Two-stage model reduction approaches for the efficient and certified solution of parametrized optimal control problems.

Hendrik Kleikamp, Lukas Renelt

CoRR, 2024

Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics.

Björn Schembera, Frank Wübbeling, Hendrik Kleikamp, Burkhard Schmidt, Aurela Shehu, Marco Reidelbach, Christine Biedinger, Jochen Fiedler, Thomas Koprucki, Dorothea Iglezakis, Dominik Göddeke

CoRR, 2024

Application of an adaptive model hierarchy to parametrized optimal control problems.

Hendrik Kleikamp

CoRR, 2024

2023

A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs.

Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler, Tizian Wenzel

SIAM J. Sci. Comput., June, 2023

Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems.

Hendrik Kleikamp, Martin Lazar, Cesare Molinari

CoRR, 2023

Ontologies for Models and Algorithms in Applied Mathematics and Related Disciplines.

Björn Schembera, Frank Wübbeling, Hendrik Kleikamp, Christine Biedinger, Jochen Fiedler, Marco Reidelbach, Aurela Shehu, Burkhard Schmidt, Thomas Koprucki, Dorothea Iglezakis, Dominik Göddeke

Proceedings of the Metadata and Semantic Research - 17th Research Conference, 2023

Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling.

Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler

Proceedings of the Large-Scale Scientific Computations - 14th International Conference, 2023

2022

Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain.

Hendrik Kleikamp, Mario Ohlberger, Stephan Rave

CoRR, 2022

Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery.

Tim Keil, Hendrik Kleikamp, Rolf J. Lorentzen, Micheal B. Oguntola, Mario Ohlberger

Adv. Comput. Math., 2022