Skip to content
Menu
Technical University of Munich
Technical University of Munich

SCCS Colloquium

The SCCS Colloquium is a forum giving students, guests, and members of the chair the opportunity to present their research insights, results, and challenges. Do you need ideas for your thesis topic? Do you want to meet your potential supervisor? Do you want to discuss your research with a diverse group of researchers, rehearse your conference talk, or simply cheer for your colleagues? Then this is the right place for you (and you are also welcome to bring your friends along).

Upcoming talks

Philipp Kutz: Evaluating Convolutional Neural Networks in Multi-Fidelity Modeling

SCCS Colloquium |

The first goal is to investigate how well Convolutional Neural Networks (CNNs) are suited for multi-fidelity (MF) modelling. The second objective is to analyse which architectures and approaches perform better and which perform worse and why there are differences between the various methods. Neural Networks (NN) can learn arbitrary discontinuous functions and can handle high dimensional data better than other regression methods. CNNs are NN, which are well-suited for image processing. MF modelling is an important component in surrogate modelling. There are two main learning styles: NN-based MF models learn the low-fidelity (LF) / high-fidelity (LF) relation either implicit or explicit. Terramechanical data with bi-fidelity tabular data and images were provided by the German Aerospace Center (DLR) for the investigations. Two main architecture types were examined: the Multi-Fidelity Data-Fusion (MF-DF) and the Transfer Learning Neural Network (TLNN) architecture type. The MF-DF architecture type was represented with the explicitly learning MDACNN architecture. The MDACNN architecture processes tabularized data. The TLNN architecture type was represented with the implicitly learning MFCNN-TL architecture and with the implicitly and explicitly learning MF-TLNN architecture. Both the MFCNN-TL and the MF-TLNN architecture process images. It can be concluded from the investigations that all main architectures (MF-DF and TLNN architecture types) and all learning types (explicit, implicit and mixed learning) are suitable for MF modelling. CNNs can be used effectively in MF models. The MF-TLNN architecture with its mixed-learning approach outperformed the other two architectures. This leads to the conclusion that the combination of explicit and implicit learning styles in a network increases learning performance. In future work, more implicit and explicit learning architectures and their combinations in the MF-TLNN architecture need to be investigated to optimise the performance of the TLNN architecture further.

Master's thesis presentation. Philipp is advised by Vladyslav Fediukov and Prof. Dr. Felix Dietrich.

◄ Back to: SCCS Colloquium

You don't want to miss a talk? Subscribe to our mailing list and our Colloquium calendar .

Previous talks →

Contribute a talk

To register and schedule a talk, you should fill the form Colloquium Registration at least four weeks before the earliest preferred date. Keep in mind that we only have limited slots, so please plan your presentation early. In special cases, contact colloquium@mailsccs.in.tum.de.

Colloquium sessions are now on-campus. We have booked room MI 00.13.054 for WS24/25. You can either bring your own laptop or send us the slides as a PDF ahead of time. The projector only has an HDMI connection, so please bring your own adapters if necessary.

Do you want to attend but cannot make it in person? We now have a hybrid option. Simply join us through this BBB room: https://bbb.in.tum.de/shu-phv-eyq-rad

We invite students doing their Bachelor's or Master's thesis, as well as IDP, Guided Research, or similar projects at SCCS to give one 20min presentation to discuss their results and potential future work. The time for this is typically after submitting your final text. Check also with your study program regarding any requirements for a final presentation of your project work.

New: In regular times, we will now have slots for presenting early stage projects (talk time 2-10min). This is an optional opportunity for getting additional feedback early and there is no strict timeline.

Apart from students, we also welcome doctoral candidates and guests to present their projects.

During the colloquium, things usually go as follows:

  • 10min before the colloquium starts, the speakers setup their equipment with the help of the moderator. The moderator currently is Ana Cukarska. Make sure to be using an easily identifiable name in the online session's waiting room.
  • The colloquium starts with an introduction to the agenda and the moderator asks the speaker's advisor/host to put the talk into context.
  • Your talk starts. The scheduled time for your talk is normally 20min with additional 5-10min for discussion.
  • During the discussion session, the audience can ask questions, which are meant for clarification or for putting the talk into context. The audience can also ask questions in the chat.
  • Congratulations! Your talk is over and it's now time to celebrate! Have you already tried the parabolic slides that bring you from the third floor to the Magistrale?

Do you remember a talk that made you feel very happy for attending? Do you also remember a talk that confused you? What made these two experiences different?

Here are a few things to check if you want to improve your presentation:

  • What is the main idea that you want people to remember after your presentation? Do you make it crystal-clear? How quickly are you arriving to it?
  • Which aspects of your work can you cover in the given time frame, with a reasonable pace and good depth?
  • What can you leave out (but maybe have as back-up slides) to not confuse or overwhelm the audience?
  • How are you investing the crucial first two minutes of your presentation?
  • How much content do you have on your slides? Is all of it important? Will the audience know which part of a slide to look at? Will somebody from the last row be able to read the content? Will somebody with limited experience in your field have time to understand what is going on?
  • Are the figures clear? Are you explaining the axes or any other features clearly?

In any case, make sure to start preparing your talk early enough so that you can potentially discuss it, rehearse it, and improve it.

Here are a few good videos to find out more:

Did you know that the TUM English Writing Center can also help you with writing good slides?

Work with us!

Do your thesis/student project in Informatics / Mathematics / Physics: Student Projects at the SCCS.

To top