Master's thesis presentation. Rodrigo is advised by Qing Sun and Prof. Dr. Felix Dietrich.
SCCS Kolloquium
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
Rodrigo Sanchez Cela: Full Waveform Inversion with Neural Network Based Ansatzfunctions
SCCS Colloquium |
Computer imaging techniques are crucial in fields like medicine and engineering, where CT scans help diagnose patients, and non-destructive testing (NDT) ensures the safety of structures such as airplane wings. In this thesis, we study the full waveform inversion (FWI) problem from a deep learning point of view. FWI, initially developed in seismology and applied recently in NDT, is explored using various neural network architectures, namely convolutional and feed-forward neural networks. Our initial goal was to apply a feed-forward neural network (FNN), along with the "Sample Where It Matters" (SWIM) weight sampling algorithm, to solve the FWI problem outlined in \cite{main}. We wanted to propose a new method for the research described in the cited article. The approach sought to reduce the number of trainable parameters and hence inference time by fixing hidden layer weights and only optimizing the output layer. However, our FNN failed to predict the objective material distribution, even after adjusting and testing out different initializations designed to make the algorithm converge easier, using different SWIM domain approximations. A supervised learning experiment confirmed the network could not approximate the discontinuous ground truth gamma using gradient descent, which explained the failure in solving the FWI problem. Seeking to determine the capabilities of neural networks to solve the FWI problem, we tested smoother ground truth functions: (i) a Gaussian and (ii) a sinusoidal function. In addition, we have also studied the effect of simulating a larger domain. Convolutional neural networks (CNNs) could solve both supervised learning and FWI tasks, independent of the discretization and domain size, whereas FNNs failed. With an extended domain and the same number of grid points as in the original experiments, an FNN with four hidden layers and 500 neurons per layer successfully handled smooth functions in the supervised learning framework, successfully solved the FWI problem for the sinusoidal-shaped function, however struggled with the Gaussian function in FWI. Lastly, we compared two CNN initializations for FWI on smooth functions: (i) Xavier-Glorot and (ii) weights trained on a void-less domain. We found that the first worked better for the Gaussian function, while the second was more effective for the sinusoidal function. In this thesis, we also discuss improvements and future work that could help answer our study's unresolved questions.
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Contribute a talk
To register and schedule a talk, you should fill the form Colloquium Registration at least two 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:
- Simon Peyton Jones: How to Give a Great Research Talk (see also How to Write a Great Research Paper)
- Susan McConnell: Designing effective scientific presentations
- Jens Weller: Presenting Code
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.