This image shows Patrick  Buchfink

Patrick Buchfink


Research assistant
Institute of Applied Analysis and Numerical Simulation
Working Group Numerical Mathematics


+49 711 685 64778
+49 711 685 65507

Pfaffenwaldring 57
70569 Stuttgart
Room: 7.116

Office Hours

By arrangement

  • 2020:
     - P. Buchfink, B. Haasdonk, S. Rave, “PSD-Greedy Basis Generation for Structure-Preserving Model Order Reduction of Hamiltonian Systems“, Proceedings Of The Conference Algoritmy, 151-160, 2020. URL:
     - P. Buchfink, B. Haasdonk, “Experimental Comparison of Symplectic and Non-symplectic Model Order Reduction on an Uncertainty Quantification Problem”, Enumath Proceedings, 2020.
  •  2019:
     - P. Buchfink, A. Bhatt, B. Haasdonk, “Symplectic Model Order Reduction with Non-Orthonormal Bases”, Mathematical and Computational Applications, 2019. DOI: 10.3390/mca24020043
  •  2018:
     - P. Buchfink, “Structure-Preserving Model Reduction of Hamiltonian Systems for Linear Elasticity”, MATHMOD 2018 Extended Abstract Volume, 2018. DOI: 10.11128/arep.55.a55228
     - P. Buchfink, “Structure-preserving Model Reduction for Elasticity”, Diploma thesis, 2018.

 • Summer term 2020: Guest lecture in "SimTech Ringvorlesung"
 • Winter term 2019: "Mathematik 1 für Wirtschaftswissenschaftler"
 • Summer term 2019: "Numerische Grundlagen für ernen, fmt, mach, mawi"
 • Winter term 2018: "Höhere Mathematik 3 für el, kyb, mecha, phys"
 • Summer term 2018: "Mathematische Programmierung für Lehramt"
 • Winter term 2017: "Programmierkurs für den Bachelor"

since 01.2018 reasearch associate at the University of Stuttgart
2016-2018 Master studies in Simulation Technology at the Univeristy of Stuttgart
2012-2016 Bachelor studies in Simulation Technology at the University of Stuttgart

2018:  Best MATHMOD Poster 2018 (1st place) for the contribution Buchfink, P. and Haasdonk, B.: Structure-preserving Model Reduction of Hamiltonian Systems for Linear Elasticity, 2018.

The prize "Best MATHMOD Poster 2018" was awarded to our contribution "Structure-preserving Model Reduction of Hamiltonian Systems for Linear Elasticity" authored by Buchfink, P. and Haasdonk, B. at the MathMod 2018. We achieved the 1st place from a total of 30 posters.

We highly acknowledge the funding by the German Research Foundation (DFG) within the Soft Tissue Robotics International Research Training Group (IRTG) and by the SimTech Industrial Consortium e.V. (IC SimTech).2018_Best_MATHMOD_Poster_2018

  • SimTech PN5-7: Uncertainty Quantification by Physics- and Data-Based Models for Mechanical Systems
  • Structure-preserving Model Order Reduction for parametric Hamiltonian systems
  • Model Order Reduction on nonlinear manifolds

• SAMM 2020: Learning Models from Data, Magdeburg (Germany), online,  (27. – 31.07.2020)
• Scientific Machine Learning Workshop, Cologne, Germany (08.–10.01.2020)
• ENUMATH 2019, Egmond aan Zee, Netherlands (30.09. – 04.10.2019)
• Port-Hamiltonian Spring School 2019, Fraueninsel (Chiemsee), Germany (30.03. – 05.04.2019)
• MOR Summer School 2018, Hamburg, Germany (24. – 28.09.2018)]
• EUROMECH Workshop 597, Bad Herrenalb, Germany (28. – 31.10.2018)
• MathMod 2018, Vienna, Austria (21. – 23.02.2018)

  • 2020: Symplectic Neural Networks, SimTech B.Sc. Thesis

RBmatlab patch: symplectic MOR An add-on to RBmatlab which contains the code required to reproduce the numerical experiments of the symplectic model reduction procedures discussed in our preprint Buchfink, P. & Bhatt, A. & Haasdonk, B.: Symplectic Model Order Reduction with Non-Orthonormal Bases

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