Postdoc

• 2024
• 2023
• 2022
• 2021
• 2019
• 2018
• 2017
• 2016
• 2015
• 2014
• 2013
• 2012
• 2009

2024

• Wenzel, Tizian, Haasdonk, Bernard, Kleikamp, Hendrik, Ohlberger, Mario, and Schindler, Felix. 2024. “Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling.” in Large-Scale Scientific Computations, Vol. 13952 of Lecture Notes in Computer Science, edited by I. Lirkov and S. Margenov. Berlin: Springer Nature. doi: 10.1007/978-3-031-56208-2_11.
• Keil, Tim, Ohlberger, Mario, and Schindler, Felix. 2024. “Adaptive Localized Reduced Basis Methods for Large Scale PDE-constrained Optimization.” in Large-Scale Scientific Computations, Vol. 13952 of Lecture Notes in Computer Science, edited by I Lirkov and S Margenov. Berlin: Springer Nature. doi: 10.1007/978-3-031-56208-2_10.
• Tim, Keil, Mario, Ohlberger, Felix, Schindler, and Julia, Schleuß. 2024. “Local training and enrichment based on a residual localization strategy.” in Proceedings of the Conference Algoritmy 2024, Vol. 8 of Proceedings of the Conference Algoritmy, edited by P Frolkovič, K Mikula and D Ševčovič. Bratislava: Jednota slovenských matematikov a fyzikov.

2023

• Haasdonk, B, Kleikamp, H, Ohlberger, M, Schindler, F, and Wenzel, T. 2023. “A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs.” SIAM Journal on Scientific Computing, № 45 (3): A1039–1065. doi: 10.1137/22M1493318.

2022

• Gavrilenko, Pavel, Haasdonk, Bernard, Iliev, Oleg, Ohlberger, Mario, Schindler, Felix, Toktaliev, Pavel, Wenzel, Tizian, and Youssef, Maha. et al. 2022. “A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows.” in Large-Scale Scientific Computing, Vol. 13127 of Lecture Notes in Computer Science (LNCS), edited by Ivan Lirkov and Svetozar Margenov. Düsseldorf: Springer VDI Verlag. doi: 10.1007/978-3-030-97549-4_43.
• Banholzer, S, Keil, T, Mechelli, L, Ohlberger, M, Schindler, F, and Volkwein, S. 2022. “An adaptive projected Newton non-conforming dual approach for trust-region reduced basis approximation of PDE-constrained parameter optimization.” Pure and Applied Functional Analysis, № 7 (5): 1561–1596.
• Fokina, D, Iliev, O, Toktaliev, P, Oseledets, I, and Schindler, F. 2022. “On the Performance of Machine Learning Methods for Breakthrough Curve Prediction.” arXiv [physics.flu-dyn], № 2204.11719 doi: 10.48550/arXiv.2204.11719.
• Haasdonk, B, Ohlberger, M, and Schindler, F. 2022. “An adaptive model hierarchy for data-augmented training of kernel models for reactive flow.” in MATHMOD 2022 Discussion Contribution Volume, edited by Felix Breitenecker, Wolfgang Kemmetmüller, Andreas Körner, Andreas Kugi and Inge Troch. Wien: ARGESIM Verlag. doi: 10.11128/arep.17.a17155.

2021

• Buhr, Andreas, Iapichino, Laura, Ohlberger, Mario, Rave, Stephan, Schindler, Felix, and Smetana, Kathrin. 2021. “Localized model reduction for parameterized problems.” in Model Order Reduction: Volume 2 Snapshot-Based Methods and Algorithms, edited by P Benner, S Grivet-Talocia, A Quarteroni, G Rozza, W Schilders and L Sileira. doi: 10.1515/9783110671490-006.
• Keil, T, Mechelli, L, Ohlberger, M, Schindler, F, and Volkwein, S. 2021. “A non-conforming dual approach for adaptive Trust-Region Reduced Basis approximation of PDE-constrained optimization.” ESAIM: Mathematical Modelling and Numerical Analysis, № 55: 1239–1269. doi: 10.1051/m2an/2021019.

2019

• Rave, Stephan Schindler Felix. 2019. “A locally conservative reduced flux reconstruction for elliptic problems.” in Special Issue: 90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), Vol. 19 , edited by J Eberhardsteiner and M Schöberl. New York City: John Wiley & Sons. doi: 10.1002/pamm.201900026.

2018

• Ohlberger, Mario, Schaefer, Michael, and Schindler, Felix. 2018. “Localized Model Reduction in PDE Constrained Optimization.” in Shape Optimization, Homogenization and Optimal Control – DFG-AIMS workshop held at the AIMS Center Senegal, March 13-16, 2017 , Vol. 169 of International Series of Numerical Mathematics, edited by V Schulz and D Seck. Basel: Birkhäuser Verlag. doi: 10.1007/978-3-319-90469-6_8.
• Ohlberger, Mario Rave Stephan, and Schindler, Felix Wedemeier Tobias. 2018. “Model reduction for parameterized systems and inverse problems.” Oberwolfach Reports, № 2018 (39): 2454–2457. doi: 10.4171/OWR/2018/39.

2017

• Ohlberger, M, and Schindler, F. 2017. “Non-Conforming Localized Model Reduction with Online Enrichment: Towards Optimal Complexity in PDE constrained Optimization.” in Finite Volumes for Complex Applications VIII - Hyperbolic, Elliptic and Parabolic Problems: FVCA 8, Lille, France, June 2017, edited by C Cancès and P Omnes. Basel: Springer International Publishing. doi: 10.1007/978-3-319-57394-6_38.
• Leibner, T, Milk, R, and Schindler, F. 2017. “Extending DUNE: The dune-xt modules.” Archive of Numerical Software, № 5 (1): 193–216. doi: 10.11588/ans.2017.1.27720.
• Ohlberger, M, Rave, S, and Schindler, F. 2017. “True Error Control for the Localized Reduced Basis Method for Parabolic Problems.” in Model Reduction of Parametrized Systems, Vol. 17 of MS&A (Modeling, Simulation and Applications), edited by P. Benner, M. Ohlberger, A. Patera, G. Rozza and K. Urban. Basel: Springer International Publishing. doi: 10.1007/978-3-319-58786-8_11.

2016

• Ohlberger, M, Rave, S, and Schindler, F. 2016. “Model Reduction for Multiscale Lithium-Ion Battery Simulation.” in Numerical Mathematics and Advanced Applications ENUMATH 2015, Vol. 112 of Lecture Notes in Computational Science and Engineering, edited by B Karasözen, M Manguoğlu, M Teuer-Sezgin, S Göktepe and Ö Uğur. Heidelberg: Springer. doi: 10.1007/978-3-319-39929-4_31.
• Schindler, F. 2016. “Model reduction for parametric multi-scale problems.” Dissertation thesis, Westfälische Wilhelms-Universität Münster.
• Milk, R, Rave, S, and Schindler, F. 2016. “pyMOR - Generic algorithms and interfaces for model order reduction.” SIAM Journal on Scientific Computing, № 38 (5): S194–S216. doi: 10.1137/15M1026614.
• Ohlberger, M, Rave, S, and Schindler, F. 2016. “Adaptive Localized Model Reduction.” Oberwolfach Reports, № 13 (3): 2406–2409. doi: 10.4171/OWR/2016/42.

2015

• Ohlberger, M, and Schindler, F. 2015. “Error control for the localized reduced basis multi-scale method with adaptive on-line enrichment.” SIAM J. Sci. Comput., № 37 (6): A2865–A2895. doi: 10.1137/151003660.

2014

• Ohlberger, M, and Schindler, F. 2014. “A-Posteriori Error Estimates for the Localized Reduced Basis Multi-Scale Method.” in Finite Volumes for Complex Applications VII-Methods and Theoretical Aspects, Vol. 77 of Springer Proceedings in Mathematics & Statistics, edited by Rohde C and ,. Basel: Springer International Publishing. doi: 10.1007/978-3-319-05684-5_41.

2013

• Albrecht, F, and Ohlberger, M. 2013. “The localized reduced basis multi-scale method with online enrichment.” Oberwolfach Reports, № 7: 406–409. doi: 10.4171/OWR/2013/07.
• Ohlberger, M, Albrecht, F, Drohmann, M, Henning, P, Kaulmann, S, and Schweizer, B. 2013. “Model reduction for multiscale problems.” Oberwolfach Reports, № 39: 2228–2230. doi: 10.4171/OWR/2013/39.

2012

• Albrecht, F, Haasdonk, B, Kaulmann, S, and Ohlberger, M. 2012. “The Localized Reduced Basis Multiscale Method.” contribution to the Algoritmy 2012, Conference on Scientific Computing, Vysoke Tatry, Podbanske, September 9-14, 2012, Vysoke Tatry, Podbanske Bratislava: Publishing House of Slovak University of Technology.

2009

• Albrecht, F. 2009. Local Discontinuous Galerkin Verfahren für die Stokes Gleichungen und Homogenisierung in porösen Medien (Diplomarbeit),