About Me
Patrick Buchfink is a postdoc at the chair of Mathematics of Systems Theory (MAST) in the Department of Applied Mathematics at the University of Twente.
His research investigates model reduction techniques which are structure-preserving. The main focus is the development of nonlinear approximation techniques for the reduction of high-dimensional Hamiltonian systems. Such systems naturally appear e.g. in the discretization of Hamiltonian partial differential equations. This research extends the work during his PhD at the University of Stuttgart.
Organisations
Publications
Recent
Buchfink, P.
, Glas, S., & Haasdonk, B. (2023).
Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds. (pp. 1-11). ArXiv.org.
https://arxiv.org/abs/2312.00724
Buchfink, P.
, Glas, S., Haasdonk, B., & Unger, B. (2023).
Model Reduction on Manifolds: A differential geometric framework. ArXiv.org.
https://arxiv.org/abs/2312.01963
Sharma, H.
, Mu, H.
, Buchfink, P., Geelen, R.
, Glas, S., & Kramer, B. (2023).
Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds.
Computer methods in applied mechanics and engineering,
417(Part A), Article 116402.
https://doi.org/10.1016/j.cma.2023.116402
Buchfink, P.
, Glas, S., & Haasdonk, B. (2023).
Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder.
SIAM journal on scientific computing,
45(2), A289-A311.
https://doi.org/10.1137/21M1466657
Buchfink, P.
, Glas, S., & Haasdonk, B. (2022).
Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems.
IFAC-papersonline,
55(20), 463-468.
https://doi.org/10.1016/j.ifacol.2022.09.138
Other Contributions
Buchfink, P., Glas, S., Haasdonk, B., (2023a). Approximation Bounds for Model Reduction on Polynomially Mapped Manifolds. Preprint. arXiv: 2312.00724 [math.NA].
Buchfink, P., Glas, S., Haasdonk, B., Unger, B., (2023). Model Reduction on Manifolds: A differential geometric framework. Preprint. arXiv: 2312.01963 [math.NA].
Herkert, R., Buchfink, P., Haasdonk, B., (2023). Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems. Preprint. arXiv: 2303.18072 [math.NA].
Herkert, R., Buchfink, P., Haasdonk, B., Rettberg, J., Fehr, J., (2023). Randomized Symplectic Model Order Reduction for Hamiltonian Systems. Preprint. arXiv: 2303.04036 [math.NA].
Rettberg, J., Wittwar, D., Buchfink, P., Herkert, R., Fehr, J., Haasdonk, B., (2023). Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems. Preprint. arXiv: 2303.17329 [math.NA].
Buchfink, P., Glas, S., Haasdonk, B., (2023b). “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder”.
In: SIAM Journal on Scientific Computing 45.2, A289–A311. doi: 10.1137/21M1466657.
Rettberg, J., Wittwar, D., Buchfink, P., Brauchler, A., Ziegler, P., Fehr, J., Haasdonk, B., (2023). “Port-Hamiltonian fluid–structure interaction modelling and structure-preserving model order reduction of a classical guitar”. In: Mathematical and Computer Modelling of Dynamical Systems 29.1, pp. 116–148. doi: 10.1080/13873954.2023.2173238.
Sharma, H., Mu, H., Buchfink, P., Geelen, R., Glas, S., Kramer, B., (2023). “Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds”. In: Computer Methods in Applied Mechanics and Engineering 417, p. 116402. doi: 10.1016/j.cma.2023.116402.
Buchfink, P., Glas, S., Haasdonk, B., (2022). “Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems”. In: Proceedings of MATHMOD 2022. Vol. 55. 20, pp. 463–468. doi: 10.1016/j.ifacol.2022.09.138.
Leiteritz, R., Buchfink, P., Haasdonk, B., Pflüger, D., (2022). “Surrogate-data-enriched Physics-Aware Neural Networks”. In: Proceedings of the Northern Lights Deep Learning Workshop 2022. Vol. 3. doi: 10.7557/18.6268.
Shuva, S., Buchfink, P., Röhrle, O., Haasdonk, B., (2022). “Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue”. In: Large-Scale Scientific Computing. Cham: Springer International Publishing, pp. 402–409. doi: 10.1007/978-3-030-97549-4_46.
Buchfink, P., Haasdonk, B., Rave, S., (2020). “PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems”. In: Proceedings of the Conference Algoritmy 2020. Vydavateľstvo SPEKTRUM, pp. 151–160. url: http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829.
Buchfink, P., Haasdonk, B., (2020). “Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem”. In: Numerical Mathematics and Advanced Applications ENUMATH 2019. Springer International Publishing. doi: 10.1007/978-3-030-55874-1_19.
Buchfink, P., Bhatt, A., Haasdonk, B., (2019). “Symplectic Model Order Reduction with Non-Orthonormal Bases”. In: Mathematical and Computational Applications 24.2. doi: 10.3390/mca24020043.
Buchfink, P., Glas, S., Haasdonk, B., Unger, B., (2023). Model Reduction on Manifolds: A differential geometric framework. Preprint. arXiv: 2312.01963 [math.NA].
Herkert, R., Buchfink, P., Haasdonk, B., (2023). Dictionary-based Online-adaptive Structure-preserving Model Order Reduction for Parametric Hamiltonian Systems. Preprint. arXiv: 2303.18072 [math.NA].
Herkert, R., Buchfink, P., Haasdonk, B., Rettberg, J., Fehr, J., (2023). Randomized Symplectic Model Order Reduction for Hamiltonian Systems. Preprint. arXiv: 2303.04036 [math.NA].
Rettberg, J., Wittwar, D., Buchfink, P., Herkert, R., Fehr, J., Haasdonk, B., (2023). Improved a posteriori Error Bounds for Reduced port-Hamiltonian Systems. Preprint. arXiv: 2303.17329 [math.NA].
Buchfink, P., Glas, S., Haasdonk, B., (2023b). “Symplectic Model Reduction of Hamiltonian Systems on Nonlinear Manifolds and Approximation with Weakly Symplectic Autoencoder”.
In: SIAM Journal on Scientific Computing 45.2, A289–A311. doi: 10.1137/21M1466657.
Rettberg, J., Wittwar, D., Buchfink, P., Brauchler, A., Ziegler, P., Fehr, J., Haasdonk, B., (2023). “Port-Hamiltonian fluid–structure interaction modelling and structure-preserving model order reduction of a classical guitar”. In: Mathematical and Computer Modelling of Dynamical Systems 29.1, pp. 116–148. doi: 10.1080/13873954.2023.2173238.
Sharma, H., Mu, H., Buchfink, P., Geelen, R., Glas, S., Kramer, B., (2023). “Symplectic model reduction of Hamiltonian systems using data-driven quadratic manifolds”. In: Computer Methods in Applied Mechanics and Engineering 417, p. 116402. doi: 10.1016/j.cma.2023.116402.
Buchfink, P., Glas, S., Haasdonk, B., (2022). “Optimal Bases for Symplectic Model Order Reduction of Canonizable Linear Hamiltonian Systems”. In: Proceedings of MATHMOD 2022. Vol. 55. 20, pp. 463–468. doi: 10.1016/j.ifacol.2022.09.138.
Leiteritz, R., Buchfink, P., Haasdonk, B., Pflüger, D., (2022). “Surrogate-data-enriched Physics-Aware Neural Networks”. In: Proceedings of the Northern Lights Deep Learning Workshop 2022. Vol. 3. doi: 10.7557/18.6268.
Shuva, S., Buchfink, P., Röhrle, O., Haasdonk, B., (2022). “Reduced Basis Methods for Efficient Simulation of a Rigid Robot Hand Interacting with Soft Tissue”. In: Large-Scale Scientific Computing. Cham: Springer International Publishing, pp. 402–409. doi: 10.1007/978-3-030-97549-4_46.
Buchfink, P., Haasdonk, B., Rave, S., (2020). “PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems”. In: Proceedings of the Conference Algoritmy 2020. Vydavateľstvo SPEKTRUM, pp. 151–160. url: http://www.iam.fmph.uniba.sk/amuc/ojs/index.php/algoritmy/article/view/1577/829.
Buchfink, P., Haasdonk, B., (2020). “Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem”. In: Numerical Mathematics and Advanced Applications ENUMATH 2019. Springer International Publishing. doi: 10.1007/978-3-030-55874-1_19.
Buchfink, P., Bhatt, A., Haasdonk, B., (2019). “Symplectic Model Order Reduction with Non-Orthonormal Bases”. In: Mathematical and Computational Applications 24.2. doi: 10.3390/mca24020043.
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Visiting Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
(building no. 11), room 0003
Hallenweg 19
7522NH Enschede
The Netherlands
Mailing Address
University of Twente
Faculty of Electrical Engineering, Mathematics and Computer Science
Zilverling
0003
P.O. Box 217
7500 AE Enschede
The Netherlands