Person

Daniel Wolff

M. Sc.

Research Associate

Daniel Wolff
Chair for Computational Analysis of Technical Systems

Address

Building: Rogowski

Room: 219a

Schinkelstr. 2

52062 Aachen

Contact

WorkPhone
Phone: +49 241 80 99920
Fax Fax: +49 241 80 99910
 

About me

I started studying Computational Engineering Science at RWTH 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.

Since March 2020 I have been working as a research associate at CATS and I am part of the HDS-LEE Graduate School.

 

Research

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

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

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

 

Supervised student theses

  • Advanced Strategies for Improving Physics-Informed Neural Networks as Reduced
    Simulation Models, Master Thesis, RWTH Aachen, 2022

  • Improving Reinforcement Learning for Shape Optimization of Profile Extrusion Die
    Flow Channels through Multi-Environment Training and Custom Flow Field Feature
    Extractors, Master Thesis, RWTH Aachen, 2022

  • Physics-Informed Neural Networks for Predicting the Flow Field in Bioreactors, Master Thesis, RWTH Aachen, 2022

  • Development of a Python Framework for Shape Optimization based on Reinforcement Learning, Seminar Thesis, RWTH Aachen, 2021

  • Shape Optimization based on Reinforcement Learning, Master Thesis, TU Vienna, 2021

  • Physics-Informed Neural Networks as Reduced Simulation Models, Seminar Thesis, RWTH Aachen, 2021

  • Automatic Mesh Generation for Thermal FEM Simulations of Laser-Irradiated Lenses, Master Thesis, RWTH Aachen, 2021