Dr.-Ing. Daniel Wolff

Dr.-Ing. Daniel Wolff

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Research Interests

In my research, I was so far concerned with the data-driven construction of reduced simulation models. The following methods were used for this purpose:

• Physics-Informed Neural Networks (PINNs)
• Reinforcement Learning

The methods were applied for shape optimization of flow channels in profile extruders as well as for the prediction of flow fields in bioreactors.

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Academic Career

I started studying Computational Engineering Science at RWTH Aachen University in October 2014. In my bachelor's degree, I broadened my knowledge in energy technology with a focus on renewable energies. After my bachelor, I continued my studies with a master in the same subject, but then focused on numerical methods for fluid dynamics simulations.

Starting in March 2020, I did my PhD as part of the HDS-LEE Graduate School, which deepened my knowledge about machine learning.

02/2024 – present	Postdoctoral Researcher, Institute for Mathematics and Computer-Based Simulation, University of the Bundeswehr Munich, Germany
03/2020 – 12/2023	Doctoral Researcher, Chair for Computational Analysis of Technical Systems, RWTH Aachen University, Germany
10/2018 – 02/2020	Master of Science, Computational Engineering Science, RWTH Aachen University, Germany
10/2014 – 06/2018	Bachelor of Science, Computational Engineering Science, RWTH Aachen University, Germany

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Publications

Peer-reviewed publications

Clemens Fricke, Daniel Wolff, Marco Kemmerling, Stefanie Elgeti. Investigation of reinforcement learning for shape optimization of 2D profile extrusion die geometries. Advances in Computational Science and Engineering, 2023, 1(1): 1-35. DOI: 10.3934/acse.2023001

Peer-Reviewed Proceedings and Book Contributions

Idzik, C. et al. (2024). Decision Support for the Optimization of Continuous Processes using Digital Shadows. In: Brecher, C., Schuh, G., van der Aalst, W., Jarke, M., Piller, F.T., Padberg, M. (eds) Internet of Production. Interdisciplinary Excellence Accelerator Series. Springer, Cham. DOI: 10.1007/978-3-031-44497-5_12

Wolff, D., Fricke, C., Kemmerling, M. and Elgeti, S. (2023), Towards shape optimization of flow channels in profile extrusion dies using reinforcement learning. Proc. Appl. Math. Mech., 22: e202200009. DOI: 10.1002/pamm.202200009

Monographs

Wolff, D. (2023). Learning-based approaches for the analysis and optimization of profile extrusion dies and bioreactors. Dissertation. RWTH Aachen University. DOI: 10.18154/RWTH-2023-10706

Wolff, D. (2020). Modern Design of a C++17 Finite Element Continuum Mechanics Simulation Code: Automatized Differentiation and Flexible Coupling Strategies. Master's Thesis. RWTH Aachen University. DOI: 10.18154/RWTH-2020-09061