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
Profile
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.
Talks
- Bayesian Neural Network Surrogates for Efficient Global Optimization of an Airfoil Geometry, ECCOMAS 2024, 07.06.2024, Lisbon, Portugal.
- Gradient Enhanced Surrogate Modeling Framework for Aerodynamic Design Optimization, AIAA Scitech 2024, 12.01.2024, Orlando FL, USA.
- 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.
Publications
2024
Data-driven aerodynamic shape design with distributionally robust optimization approaches Journal Article
In: Computer Methods in Applied Mechanics and Engineering, vol. 429, pp. 117131, 2024, ISSN: 0045-7825.
Gradient Enhanced Surrogate Modeling Framework for Aerodynamic Design Optimization Journal Article
In: AIAA 2024-2670, 2024.
2023
Data-driven aerodynamic shape design with distributionally robust optimization approaches Miscellaneous
arXiv:2310.08931, 2023.
Trailing-Edge Noise Reduction using Porous Treatment and Surrogate-based Global Optimization Miscellaneous
arXiv:2301.13047, 2023.
2021
2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR), 2021.