Dr. Stefan Wittek

Since 04/2023:

Administrator of a professorship - Methods and Applications of Machine Learning
Institute for Software and Systems Engineering - TU Clausthal

• Independent implementation of courses:
  • Fundamentals of AI
  • Applied Deep Learning
  • Introduction to computer science
  • Interdisciplinary digitization project: Software development
  • Various other student projects and theses
• Head of the research group "Machine Learned Models for Engineers"
• Project acquisition

06/2021 - 03/2023:

Postdoc
Institute for Software and Systems Engineering - TU Clausthal

• Establishment and management of the research group "Machine Learned Models for Engineers"
• Independent realization of courses.
• Project acquisition (not as PI): More than 2 million €

10/2013 - 05/2021:

Research assistant
Institute for Software and Systems Engineering - TU Clausthal

• Research group "Applied Machine Learning and Simulation"
• Participation in the acquisition of more than € 1.9 million in third-party funding
• Supervision of courses including Computer Science I, Programming Course, Software Engineering I, Requirement Engineering and Project and Quality Management
• Research in the projects, among others:
  • Intelligent control systems (VW): Robust extrapolation for neural networks
  • Multi-level simulation: Development of a concept for multi-level simulation
  • Mobil4e: Co-simulation platform for heterogeneous models

Eivazi, H., Wittek, S., Rausch, A, 2024. Nonlinear model reduction for operator learning. ICLR 2024, Tiny Papers (accepted, invite to present (notable))

Eivazi, H., Tröger, J.-A., Wittek, S., Hartmann, S., Rausch, A, 2023. FE2 Computations with Deep Neural Networks: Algorithmic Structure, Data Generation, and Implementation. In Mathematical and Computational Applications, vol. 28, no. 4, p. 91, Aug. 2023, doi: 10.3390/mca28040091

Morer, F. E., Wittek, S., & Rausch, A., 2023. Assessment of the suitability of degradation models for the planning of CCTV inspections of sewer pipes. Urban Water Journal, pp. 1-14, Nov. 2023, doi: 10.1080/1573062X.2023.2282126

Bratzel, D., Wittek, S., Rausch, A., Treutler, K., Gehling, T., & Wesling, V. Usage of Machine Learning for Subtopology Detection in Wire and Arc Additive Manufacturing. Proceedings of ADAPTIV 2022, pp. 32-37.

Erbel, J., Wittek, S., Grabowski, J., Rausch, A., 2020. Dynamic Management of Multi-level-simulation Workflows in the Cloud, in: Communications in Computer and Information Science. Springer, pp. 21-38. doi.org/10.1007/978-3-030-45718-1_2

Wittek, S., Rausch, A., 2018. Learning state mappings in multi-level simulation, in: Communications in Computer and Information Science. Springer, Cham, pp. 208-218. doi.org/10.1007/978-3-319-96271-9_13

Wittek, S. H.A., Göttsche, M., Rausch, A., Grabowski, J., 2016. Towards multi-level simulation using dynamic cloud environments, Proceedings of the 6th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH). IEEE, Lisbon, pp. 1-7.