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)

Nonlinear Model Order Reduction using Diffeomorphic Transformations of a Space-Time Domain. - Hendrik Kleikamp; Mario Ohlberger; Stephan Rave (CoRR, 2022)

Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling. - Tizian Wenzel; Bernard Haasdonk; Hendrik Kleikamp; Mario Ohlberger; Felix Schindler (LSSC, 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 001; Thomas Koprucki; Dorothea Iglezakis; Dominik Göddeke (MTSR, 2023)

Be greedy and learn: efficient and certified algorithms for parametrized optimal control problems. - Hendrik Kleikamp; Martin Lazar; Cesare Molinari (CoRR, 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., 2023)

Towards a Knowledge Graph for Models and Algorithms in Applied Mathematics. - Björn Schembera; Frank Wübbeling; Hendrik Kleikamp; Burkhard Schmidt 001; Aurela Shehu; Marco Reidelbach; Christine Biedinger; Jochen Fiedler; Thomas Koprucki; Dorothea Iglezakis; Dominik Göddeke (MTSR, 2024)

Application of an adaptive model hierarchy to parametrized optimal control problems. - Hendrik Kleikamp (CoRR, 2024)

Kernel Methods in the Deep Ritz framework: Theory and practice. - Hendrik Kleikamp; Tizian Wenzel (CoRR, 2024)

Adaptive Model Hierarchies for Multi-Query Scenarios. - Hendrik Kleikamp; Mario Ohlberger (CoRR, 2024)

Two-Stage Model Reduction Approaches for the Efficient and Certified Solution of Parametrized Optimal Control Problems. - Hendrik Kleikamp; Lukas Renelt (J. Sci. Comput., 2025)