Jan Rottmayer

Chair for Scientific Computing
TU Kaiserslautern
Paul-Ehrlich-Straße 34 / Geb. 36
D-67663 Kaiserslautern

Office: 36-410
Phone: +49 (0)631 205 5639
Email: jan.rottmayer@scicomp.uni-kl.de



I am a passionate researcher with a focus on data-driven modeling and machine learning. My research interests include surrogate modeling, surrogate-based optimization, data-driven modeling, and generative models. With a background in mechanical engineering and a strong work ethic developed through my years as a competitive swimmer, I bring a unique perspective to my research. I completed my bachelor’s studies through a dual study system with BASF, Germany’s largest chemical company, and finished my master’s studies on the topic of reduced order modeling and optimal flow control. I have also published a student project on the generation of synthetic ground penetrating radargrams using generative adversarial networks (GANs). I am proficient in programming with Python and have a growing enthusiasm for the rising popularity of Julia in my current work on surrogate-based optimization. Thank you for visiting my profile and feel free to reach out for further collaboration or inquiries.


  • Multi-Fidelity Aerodynamic Design Optimization Framework using Gradient Asissted
    Surrogate Modeling
    , EUROGEN 2023, 02.06.2023, Crete, Greece.
  • Trailing Edge Noise Reduction by Porous Treatment using Derivative-Free Optimization, Scientific Computing Seminar, 05.01.2023, Kaiserslautern, Germany.
  • Flow Control via Reduced Order Models, Scientific Computing Seminar, 18.11.2021, Kaiserslautern, Germany.
  • Reduced Order Modeling and Nonlinear System Identification Techniques for Fluid Dynamics, Scientific Computing Seminar, 22.04.2021, Kaiserslautern, Germany.



L. Chen, J. Rottmayer, L. Kusch, N.R. Gauger, Y. Ye

Data-driven aerodynamic shape design with distributionally robust optimization approaches Miscellaneous

arXiv:2310.08931, 2023.

Links | BibTeX

J. Rottmayer, E. Özkaya, S. Satcunanathan, B. Y. Zhou, M. Aehle, N. R. Gauger, M. Meinke, W. Schröder, S. Pullin

Trailing-Edge Noise Reduction using Porous Treatment and Surrogate-based Global Optimization Miscellaneous

arXiv:2301.13047, 2023.

Links | BibTeX


A. Fazeel, J. Rottmayer, R.Mehta; N. Bajcinca

GPR-GANs: Generation of Synthetic Ground Penetrating Radargrams Using Generative Adversarial Networks Conference

2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), 2021.

Links | BibTeX