Profile

I am a researcher in data-driven modeling and machine learning, with a focus on surrogate modeling and surrogate-based optimization. My current work investigates Bayesian Optimization using Bayesian Neural Networks for expensive black-box optimization tasks, including aerodynamic shape optimization.
I hold a background in mechanical engineering, having completed my bachelor’s studies through a dual study program with BASF, Germany’s largest chemical company. My master’s research focused on reduced-order modeling and optimal flow control. In addition, I have published work on the generation of synthetic ground-penetrating radargrams using generative adversarial networks (GANs).

I am also associated with the DFG-Graduiertenkolleg 2982 “Mathematics of Interdisciplinary Multiobjective Optimization (MIMO)”.

Talks

• Solving Distributionally Robust Optimization Problems Efficiently via Surrogate Models, EUROGEN, 17.09.2025, Lahti, Finnland.
• Gradient-enhanced BNN Surrogate Models for Aerodynamic Shape Optimization, AG Turbo, 06.03.2025, Cologne, Germany.
• Sobolev Learning for Bayesian Neural Network Assisted Aerodynamic Shape Optimization, STAB Konferenz, 13.11.2024, Regensburg, Germany.
• Bayesian Neural Network Surrogate Models for Aerodynamic Shape Optimization, AG Turbo, 08.10.2024, Dresden, Germany.
• Bayesian Neural Network Surrogates for Efficient Global Optimization of an Airfoil Geometry, ECCOMAS 2024, 07.06.2024, Lisbon, Portugal.
• Neural Network Surrogates for Efficient Global Optimization, AG Turbo, 07.03.2024, Darmstadt, Germany.
• 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

@misc{SU22025,

title = {SU2 version 8.3.0 "Harrier"},

author = {A. Cappiello, A. Rausa, B. Munguía, D. Divyaprakash, E. van der Weide, E. C. Bunschoten, F. A. van Steen, J. Rottmayer, J. Blühdorn, J. A. Kelly, L. Bachmann, M. Sagebaum, N. A. Beishuizen, O. Burghardt, P. Gomes, R. Khedkar, V. Wani, SU2 Foundation, all SU2 authors},

doi = {10.5281/zenodo.17209057},

year  = {2025},

date = {2025-08-31},

urldate = {2025-08-31},

note = {software},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Adjoint-Based Aerodynamic Shape Optimization with a Manifold Constraint Learned by Diffusion Models},

author = {L. Chen, E. Özkaya, J. Rottmayer, N.R. Gauger, Z. Shen, Y. Ye},

url = {https://arxiv.org/abs/2507.23443},

year  = {2025},

date = {2025-08-01},

urldate = {2025-08-01},

howpublished = {arXiv:2507.23443},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@article{nokeyr,

title = {Data-driven aerodynamic shape design with distributionally robust optimization approaches},

author = {L. Chen, J. Rottmayer, L. Kusch, N. R.Gauger, Y. Ye, },

url = {https://doi.org/10.1016/j.cma.2024.117131},

issn = {0045-7825},

year  = {2024},

date = {2024-06-15},

urldate = {2024-06-15},

journal = {Computer Methods in Applied Mechanics and Engineering},

volume = {429},

pages = {117131},

abstract = {We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. DRO aims to minimize the worst-case expected performance in a set of distributions that is informed by observed data with uncertainties. Building on the findings of the work Gotoh, et al. (2018), we study the connections between a class of DRO and robust design optimization, which is classically based on the mean–variance (standard deviation) optimization formulation pioneered by Taguchi. Our results provide a new perspective to the understanding and formulation of robust design optimization problems. It enables data-driven and statistically principled approaches to quantify the trade-offs between robustness and performance, in contrast to the classical robust design formulation that captures uncertainty only qualitatively. Our preliminary computational experiments on aerodynamic shape optimization in transonic turbulent flow show promising design results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Gradient Enhanced Surrogate Modeling Framework for Aerodynamic Design Optimization},

author = {E. Özkaya , J. Rottmayer, N.R. Gauger},

doi = {10.2514/6.2024-2670},

year  = {2024},

date = {2024-01-04},

urldate = {2024-01-04},

journal = {AIAA 2024-2670},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@misc{CRKGY2023,

title = {Data-driven aerodynamic shape design with distributionally robust optimization approaches},

author = {L. Chen, J. Rottmayer, L. Kusch, N.R. Gauger, Y. Ye},

url = {https://arxiv.org/pdf/2310.08931.pdf},

year  = {2023},

date = {2023-10-13},

urldate = {2023-10-13},

howpublished = {arXiv:2310.08931},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@misc{nokey,

title = {Trailing-Edge Noise Reduction using Porous Treatment and Surrogate-based Global Optimization},

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

},

url = {https://arxiv.org/abs/2301.13047},

year  = {2023},

date = {2023-01-30},

urldate = {2023-01-30},

howpublished = {arXiv:2301.13047},

keywords = {},

pubstate = {published},

tppubtype = {misc}

}

@conference{nokey,

title = {GPR-GANs: Generation of Synthetic Ground Penetrating Radargrams Using Generative Adversarial Networks},

author = {A. Fazeel, J. Rottmayer, R.Mehta and N. Bajcinca},

url = {https://ieeexplore.ieee.org/document/9843171},

doi = {10.1109/IWAGPR50767.2021.9843171},

year  = {2021},

date = {2021-00-00},

booktitle = {2021 11th International Workshop on Advanced Ground Penetrating Radar (IWAGPR)},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}