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

Aditya Phopale: Using neural networks with domain decomposition to solve partial differential equations

SCCS Colloquium |


Solving Partial Differential Equations (PDEs) is fundamental to understanding various physical phenomena across various disciplines. Traditional solvers in industry often face challenges such as computational complexity, mesh generation, and convergence issues. In recent years, neural networks have emerged as a promising alternative for tackling PDEs due to their ability to approximate complex functions and learn underlying patterns from data. This thesis presents novel advancements in solving PDEs using neural networks, coupled with innovative approaches in network initialization and domain decomposition techniques. Firstly, a neural network-based solver is introduced to solve PDEs efficiently over square domains. Employing a neural network architecture with a single hidden layer, emphasis is placed on training only the final layer to directly solve PDEs without the need for explicit data. This methodology handles non-time-dependent PDEs with boundary conditions, including linear and non-linear equations. Secondly, the thesis explores domain decomposition strategies combined with neural networks for tackling complex PDE problems. By partitioning the domain into subdomains, each has its neural network approximator. The coupling of neural networks at shared boundary points ensures solution continuity over the entire domain and accuracy in the overall solution. Furthermore, we dive into the "Sampling Where It Matters" (SWIM) concept, aimed at enhancing the initialization of neural networks for improved performance in various tasks, including PDE solving. SWIM leverages the gradient of the truth function along with input points to intelligently initialize network parameters, leading to more efficient convergence and enhanced solution accuracy. Through several experiments, the efficacy and versatility of the proposed methodologies are demonstrated, including a comparative study against existing methods such as Physics-Informed Neural Network (PINN) and Finite Element Method (FEM). This comparative analysis showcases where the presented method stands against existing methods, providing valuable insights into their performance and applicability.

Master's thesis presentation. Aditya is advised by  Dr. Dirk Hartmann and Prof. Dr. Felix Dietrich.


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

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 02.07.023 for SS2024. 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.