Dr.-Ing. Daniel Wolff

BAU 1 - Institut für Mathematik und Computergestützte Simulation
Gebäude 41/100, Zimmer 5124
089/6004 4449

Dr.-Ing. Daniel Wolff

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.

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


Peer-reviewed publications

Veronika Trávníková, Daniel Wolff, Nico Dirkes, Stefanie Elgeti, Eric von Lieres, Marek Behr. A model hierarchy for predicting the flow in stirred tanks with physics-informed neural networks. Advances in Computational Science and Engineering, 2024, DOI: 10.3934/acse.2024007
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


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