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.

Teaching

Publications

Peer-reviewed publications

Mattuschka, M., An der Lan, N., von Danwitz, M., Wolff, D., & Popp, A. (2026). Goal-oriented optimal sensor placement for PDE-constrained inverse problems in crisis management. Computer Assisted Methods in Engineering and Science, 33(2), 147–170. : 10.24423/cames.2026.1887

Trávníková, V., Wolff, D., Dirkes, N., Elgeti, S., von Lieres, E., & Behr, M. (2024). A model hierarchy for predicting the flow in stirred tanks with physics-informed neural networks. Advances in Computational Science and Engineering, 2(2), 91–129. : 10.3934/acse.2024007

Fricke, C., Wolff, D., Kemmerling, M., & Elgeti, S. (2023). Investigation of reinforcement learning for shape optimization of 2D profile extrusion die geometries. Advances in Computational Science and Engineering, 1(1), 1–35. : 10.3934/acse.2023001

Peer-Reviewed Proceedings and Book Contributions

Sahin, T., Wolff, D., & Popp, A. (2025). Physics-Informed Neural Networks for Solving Contact Problems in Three Dimensions. In Advances and Challenges in Computational Mechanics (pp. 419–431). Springer Nature Switzerland. : 10.1007/978-3-031-93213-7_33

Sahin, T., Wolff, D., von Danwitz, M., & Popp, A. (2024). Towards a Hybrid Digital Twin: Fusing Sensor Information and Physics in Surrogate Modeling of a Reinforced Concrete Beam. In 2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF) (pp. 1–8). 2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE. : 10.1109/sdf63218.2024.10773885

Idzik, C., Hilger, D., Hosters, N., Kemmerling, M., Niemietz, P., Ortjohann, L., Sasse, J., Serafeim, A., Wang, J., Wolff, D., & Hirt, G. (2023). Decision Support for the Optimization of Continuous Processesusing Digital Shadows. In Interdisciplinary Excellence Accelerator Series (pp. 281–301). Springer International Publishing. : 10.1007/978-3-031-44497-5_12

Wolff, D., Fricke, C., Kemmerling, M., & Elgeti, S. (2023). Towards shape optimization of flow channels in profile extrusion dies using reinforcement learning. In PAMM (Vol. 22, Issue 1). Wiley. : 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. : 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. : 10.18154/RWTH-2020-09061