Scientific Computing Seminar

Date and Place: Thursdays and hybrid (live in 32-349/online via Zoom). For detailed dates see below!

Content

In the Scientific Computing Seminar we host talks of guests and members of the SciComp team as well as students of mathematics, computer science and engineering. Everybody interested in the topics is welcome.

List of Talks

  • Mon
    09
    Oct
    2023
    Fri
    09
    Feb
    2024

    Prof. Dr. Nicolas Gauger, Chair for Scientific Computing (SciComp), TU Kaiserslautern

    SciComp Seminar Series

    Please contact Prof. Gauger, if you want to register for an online talk in our SciComp Seminar Series or just to register for the seminar.

    A list of the already scheduled talks can be found –> here:

  • Wed
    11
    Oct
    2023

    16:00Hybrid (Room 32-349 and via Zoom)

    Amin Jafarimoghaddam, Department of Aerospace Engineering, Universidad Carlos III, Madrid, Spain

    Title: From Residual Neural Networks to Neural ODEs – theory and applications

    Abstract:

    This talk explores key developments in neural network architectures. The discussion begins with an examination of Residual Neural Networks and their relation to Neural Ordinary Differential Equations (Neural ODEs). We briefly review some of the advantages, and limitations of Neural ODEs. We also consider possible variations of Residual Neural Networks and their higher-order Neural ODE representation. Furthermore, we explore the possibility of networks equipped with average nonlinear programming techniques, which might be alternatives to addressing the deep learning issues. Finally, we highlight some potential practical applications.

    How to join online

    You can join online via Zoom, using the following link:
    https://uni-kl-de.zoom.us/j/63123116305?pwd=Yko3WU9ZblpGR3lGUkVTV1kzMCtUUT09

  • Thu
    12
    Oct
    2023

    11:45Hybrid (Room 32-349 and via Zoom)

    Lukas Baumgärtner, Department of Mathematics, Humboldt University Berlin
    and
    PD Dr. Stephan Schmidt, Department of Mathematics, University of Trier

    Title: Using Transport Equations for Image Processing and Mesh Generation in Medical Applications

    Abstract:

    We consider the use of transport equations for solving medical image registration problem by determining the best optical flow between different brain MRI scans. In a follow-up procedure, this registration data is used to make a default generic brain mesh patient specific.
    In the ensuing optimal control problem, the transport equation is solved via an upwind scheme in a DG discretization, which usually introduces non-smoothness due to absolute values. To avoid this, we propose a smoothed version of the upwind scheme which is by construction consistent with the transport equation. Its L2-stability can be shown similarly to the regular upwind scheme by a von Neumann stability analysis, however, under a slightly different CFL-condition. For the linear transport equation, this yields a reasonable scheme by the Lax equivalence theorem.
    Numerical results are presented for the image registration problem. The corresponding mesh deformations are compared to state-of-the-art brain meshing software FreeSurfer. Towards the end of this talk, non-smoothness of the regular upwind scheme is discussed in the context of the non-linear PDEs of fluid mechanics.

    How to join online

    You can join online via Jitsi, using the following link:
    https://jitsi.rptu.de/scicomp_seminar

  • Wed
    18
    Oct
    2023

    13:00Room 34-217

    Ket Yee Lee, RHRZ@RPTU and University of Applied Sciences Kaiserslautern

    Title: Data Preprocessing Using NLP for Ticket Dispatching with Classical Classification Methods

    Abstract:

    Writing a support request from a customer perspective can be challenging. From a data science point of view, processing them for machine learning can be challenging as well. In this master thesis, the methods from Natural Language Processing (NLP) are applied to prepare the assignment of service requests into the respective associated queues in a machine-understandable way. Texts are cleaned and qualified using tokenization and part-of-speech tagging (POS) with the German Stuttgart-Tübingen tag set (STTS).
    The classical classification methods Naïve Bayes, Decision Tree, Logistic Regression, Super Vector Machine, k Nearest Neigbor and the ensemble methods Random Forest, Gradient Boosting and Tree Ensemble are contrasted.
    Dispatching of customer queries written in natural language is possible using classical classification methods. The proof-of-concept shows that with Logistic Regression, for the available database, tickets can be assigned by machine with 64.5% accuracy for the top 10 queues.

  • Thu
    09
    Nov
    2023

    11:45Hybrid (Room 32-349 and via Zoom)

    Dr. Kazuki Hayashi , Assistant Professor, Department of Architecture and Architectural Engineering, Kyoto University, Japan

    Title: Extracting meaningful features from data with irregular connectivity for structural optimization

    Abstract:

    The data handled by machine learning is not necessarily aligned in a rectangular array like raster images. In this case, the data can be represented as graphs consisting of vertices and edges. However, capturing important features from graphs is a challenging problem. This talk discuss graph embedding, a technique used to extract features from graphs, and explore case studies that demostrate the usefulness of graph embedding in solving structural optimization problems, including topology optimization of trusses, sizing optimization of steel frames, and assembly sequence optimization.

    How to join online

    You can join online via Zoom, using the following link:
    https://uni-kl-de.zoom.us/j/63123116305?pwd=Yko3WU9ZblpGR3lGUkVTV1kzMCtUUT09

  • Wed
    13
    Dec
    2023

    13:45Room 32-349

    The goal of the workshop is to give an overview of recent research activities at SciComp. In addition, collaborators from Fraunhofer ITWM, Trier University and MTU Aero Engines will give invited presentations for scientific exchange. Finally, we will brainstorm about future collaboration.

    Program

    13:15-15:00 Scientific Short Presentations – SciComp (5+5 minutes each)
    (Dr. E. Özkaya, Dr. M. Sagebaum, Dr. L. Kusch, O. Burghardt, R. Pochampalli, J. Blühdorn, G. Suarez, Dr. L. Chen, M. Aehle, J. Rottmayer)

    15:00-15:30 Coffee break

    Keynote Talk
    15:30-16:45 Dr. A. Linke (SciComp)

    16:45-17:30 Scientific Short Presentations – Guests (10+5 minutes each)
    (Dr. J. Kuhnert (ITWM), PD Dr. S. Schmidt (Trier U), C. Battistoni (MTU))

    17:30-18:00 Brainstorming/Thoughts on Future Collaboration (Prof. N. Gauger)

    18:00 Workshop Dinner

  • Wed
    13
    Dec
    2023

    15:30Hybrid (Room 32-349 and via Zoom)

    Dr. Alexander Linke , Chair for Scientific Computing (SciComp), University of Kaiserslautern-Landau (RPTU)

    Title: On the Discretization of the Incompressible Navier–Stokes Equations, an Elephant in the Room, and a Conceptual Update for Discretizing Constrained PDEs

    Abstract:

    The dynamics of the incompressible Navier-Stokes equations are closely related to equivalence classes of forces and an associated semi-norm, in the kernel of which all gradient fields lie. The corresponding numerical treatment of gradient fields in the momentum balance, which only change the pressure but not the velocity of an incompressible flow, has been repeatedly addressed in research since the 1980s, and a number of very different algorithmic approaches have been proposed to avoid this numerical source of error. For many years, however, the research question remained the proverbial ‘elephant in the room’, whose relevance for simulation practice was assessed very differently in the research community. The talk provides an overview of the historical development of the research question and discusses its practical relevance based on completely different physical regimes such as hydrostatics and high Reynolds number vortex flows. In particular, it discusses how recently developed pressure-robust methods using H(div)-conforming finite elements could solve the numerical challenge in a fundamental way and thus contribute to the solution of other previously unsolved problems. Furthermore, a conceptual update for the discretization of PDEs with constraints is proposed, which replaces the historical Stokes model problem of classical mixed finite element theory by a set of model problems from which the relevance of H(div)-conforming algorithms for the discretization of incompressible flows immediately emerges. Finally, considerations on the relevance of the obtained results for the numerical treatment of related problems, such as the compressible Navier–Stokes equations, are discussed.

    How to join online

    You can join online via Zoom, using the following link:
    https://uni-kl-de.zoom.us/j/63123116305?pwd=Yko3WU9ZblpGR3lGUkVTV1kzMCtUUT09