Publications

Patrick Buchfinck, Silke Glas, and Bernard Haasdonk. Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems. In submitted (Eds.), 2022. [PUMA: from:britsteiner ians anm]

Bernard Haasdonk. Model Order Reduction, Applications, MOR Software. In De Gruyter (Eds.), (3)De Gruyter, 2021. [PUMA: from:britsteiner ians anm fis]

Tobias Ehring, and Bernard Haasdonk. Feedback control for a coupled soft tissue system by kernel surrogates. Coupled Problems 2021, IS112021. [PUMA: unibibliografie from:britsteiner ians anm]

Tobias Ehring, and Bernard Haasdonk. Feedback control for a coupled soft tissue system by kernel surrogates. Coupled Problems 2021, 2021. [PUMA: unibibliografie robotics tissue from:britsteiner soft ians anm]

Tobias Ehring, and Bernard Haasdonk. Feedback control for a coupled soft tissue system by kernel surrogates. 2021. [PUMA: from:britsteiner ians anm]

Tobias Ehring, and Bernard Haasdonk. Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems. In submitted (Eds.), 2021. [PUMA: from:britsteiner ians anm]

Tobias Ehring, and Bernard Haasdonk. Greedy sampling and approximation for realizing feedback control for high dimensional nonlinear systems. 2021. [PUMA: from:britsteiner ians anm]

A. Bhatt, J. Fehr, and B. Haasdonk. Model order reduction of an elastic body under large rigid motion. In Springer (Eds.), Proceedings of ENUMATH 2017, (Lect. Notes Comput. Sci. Eng.,)126Springer, 2019. [PUMA: unibibliografie EXC310 liste from:britsteiner ians fis] URL

Roman Föll, Bernard Haasdonk, Markus Hanselmann, and Holger Ulmer. Deep Recurrent Gaussian Process with Variational Sparse Spectrum Approximation. 2019. [PUMA: from:britsteiner ians anm] URL

A. Bhatt, J. Fehr, D. Grunert, and Bernard Haasdonk. A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. In J. Fehr, and B. Haasdonk (Eds.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018, Springer, 2019. [PUMA: CCMOR imported haasdonk fehr (DFG) from:mathematik from:britsteiner grunert ians anm bhatt]

A. Bhatt, J. Fehr, D. Grunert, and Bernard Haasdonk. A Posteriori Error Estimation in Model Order Reduction of Elastic Multibody Systems with Large Rigid Motion. In J. Fehr, and B. Haasdonk (Eds.), IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22–25, 2018, Springer, 2018. [PUMA: CCMOR fehr from:britsteiner grunert ians bhatt imported haasdonk (DFG) anm]

Bernard Haasdonk, Boumediene Hamzi, Gabriele Santin, and Dominik Wittwar. Greedy kernel methods for center manifold approximation. Spectral and high order methods for partial differential equations---ICOSAHOM 2018, (134):95--106, Springer, Cham, 2020. [PUMA: imported unibibliografie liste from:britsteiner ians anm fis] URL

Dominik Wittwar, and Bernard Haasdonk. Convergence rates for matrix P-greedy variants. Numerical mathematics and advanced applications---ENUMATH 2019, (139):1195--1203, Springer, Cham, 2021. [PUMA: unibibliografie from:britsteiner ians fis] URL

Dominik Wittwar, and Bernard Haasdonk. Convergence rates for matrix P-greedy variants. Numerical mathematics and advanced applications---ENUMATH 2019, (139):1195--1203, Springer, Cham, 2021. [PUMA: fis from:britsteiner ians unibibliografie] URL

Alessandro Alla, Bernard Haasdonk, and Andreas Schmidt. Feedback control of parametrized PDEs via model order reduction and dynamic programming principle. Adv. Comput. Math., (46)1:Paper No. 9, 28, 2020. [PUMA: unibibliografie from:britsteiner ians fis] URL

IUTAM Symposium on Model Order Reduction of Coupled Systems, Stuttgart, Germany, May 22-25, 2018: MORCOS 2018. In Jörg Fehr, and Bernard Haasdonk (Eds.), IUTAM Bookseries, Springer, 2020. [PUMA: unibibliografie from:britsteiner ians fis]

Dennis Grunert, Jörg Fehr, and Bernard Haasdonk. Well-scaled, a-posteriori error estimation for model order reduction of large second-order mechanical systems. ZAMM, (100)8:e201900186, Wiley, 2020. [PUMA: ubs_20013 ubs_20011 liste from:britsteiner ians ubs_40177 fis unibibliografie oa ubs_30123 ubs_10008 ubs_40305 ubs_10007 ubs_30115 wos mult]

B. Haasdonk, B. Hamzi, G. Santin, and D. Wittwar. Kernel methods for center manifold approximation and a weak data-based version of the center manifold theorem. Phys. D, (427):Paper No. 133007, 14, 2021. [PUMA: imported unibibliografie from:britsteiner ians fis] URL

Raphael Leiteritz, Patrick Buchfink, Bernard Haasdonk, and Dirk Pflüger. Surrogate-data-enriched Physics-Aware Neural Networks. 2021. [PUMA: unibibliografie pn6 pn5 prePrint from:britsteiner ians exc2075 fis]

Bernard Haasdonk, Mario Ohlberger, and Felix Schindler. An adaptive model hierarchy for data-augmented training of kernel models for reactive flow. arXiv, 2021. [PUMA: unibibliografie from:britsteiner ians fis] URL