Recent Publications of Dr. Felix Schindler
$\bullet $ Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, and Felix Schindler. Application of deep kernel models for certified and adaptive RB-ML-ROM surrogate modeling. In Ivan Lirkov and Svetozar Margenov, editors, Large-Scale Scientific Computations, 117–125. Springer Nature Switzerland, May 2024. doi:10.1007/978-3-031-56208-2_11.
$\bullet $ Tim Keil, Mario Ohlberger, Felix Schindler, and Julia Schleuß. Local training and enrichment based on a residual localization strategy. arXiv e-prints, April 2024. arXiv:2404.16537.
$\bullet $ Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, Felix Schindler, and Tizian Wenzel. A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs. SIAM J. Sci. Comput., pages A1039–A1065, June 2023. doi:10.1137/22M1493318.
$\bullet $ Tim Keil, Mario Ohlberger, and Felix Schindler. Adaptive localized reduced basis methods for large scale parameterized systems. arXiv e-prints, March 2023. arXiv:2303.03074.
$\bullet $ Tizian Wenzel, Bernard Haasdonk, Hendrik Kleikamp, Mario Ohlberger, and Felix Schindler. Application of deep kernel models for certified and adaptive RB-ML-ROM surrogate modeling. arXiv e-prints, February 2023. arXiv:2302.14526.
$\bullet $ M. Ohlberger, S. Banholzer, B. Haasdonk, T. Keil, H. Kleikamp, L. Mechelli, M. Oguntola, F. Schindler, S. Volkwein, and T. Wenzel. Model reduction and learning for PDE constrained optimization. Oberwolfach Reports, 2023.
$\bullet $ Stefan Banholzer, Tim Keil, Mario Ohlberger, Luca Mechelli, Felix Schindler, and Stefan Volkwein. An adaptive projected Newton non-conforming dual approach for trust-region reduced basis approximation of PDE-constrained parameter optimization. Pure Appl. Funct. Anal., 7(5):1561–1596, October 2022. URL: yokohamapublishers.jp/online2/oppafa/vol7/p1561.html.
$\bullet $ Bernard Haasdonk, Mario Ohlberger, and Felix Schindler. An adaptive model hierarchy for data-augmented training of kernel models for reactive flow. In ARGESIM Report 17, 67–68. July 2022. doi:10.11128/arep.17.a17155.
$\bullet $ Daria Fokina, Oleg Iliev, Pavel Toktaliev, Ivan Oseledets, and Felix Schindler. On the performance of machine learning methods for breakthrough curve prediction. arXiv e-prints, April 2022. arXiv:2204.11719.
$\bullet $ Pavel Gavrilenko, Bernard Haasdonk, Oleg Iliev, Mario Ohlberger, Felix Schindler, Pavel Toktaliev, Tizian Wenzel, and Maha Youssef. A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows. In Large-Scale Scientific Computing, 378–386. March 2022. doi:10.1007/978-3-030-97549-4_43.
$\bullet $ Tim Keil, Luca Mechelli, Mario Ohlberger, Felix Schindler, and Stefan Volkwein. A non-conforming dual approach for adaptive Trust-Region reduced basis approximation of PDE-constrained parameter optimization. ESAIM: M2AN, 55(3):1239–1269, May 2021. doi:10.1051/m2an/2021019.
$\bullet $ Andreas Buhr, Laura Iapichino, Mario Ohlberger, Stephan Rave, Felix Schindler, and Kathrin Smetana. Localized model reduction for parameterized problems. In Model order reduction. Volume 2: Snapshot-based methods and algorithms, pages 245–305. January 2021. doi:10.1515/9783110671490-006.
$\bullet $ Stefan Banholzer, Tim Keil, Luca Mechelli, Mario Ohlberger, Felix Schindler, and Stefan Volkwein. An adaptive projected Newton non-conforming dual approach for trust-region reduced basis approximation of PDE-constrained parameter optimization. arXiv e-prints, December 2020. arXiv:2012.11653.
$\bullet $ Stephan Rave and Felix Schindler. A locally conservative reduced flux reconstruction for elliptic problems. PAMM, November 2019. doi:10.1002/pamm.201900026.