P. Buchfink MSc (Patrick)

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.