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:

Michael Urs Lars Kastor, Numerical Simulation Group, RPTU Kaiserslautern-Landau

Title: PIDE-model for phenotypic plasticity of glioblastoma

Abstract:

Glioblastoma is the most common aggressive type of brain cancer making up approximatly
14% of tumors originating in the brain. Despite aggressive treatment approaches, tumor
recurrence is most likely due to intra-tumoral phenotypic heterogeneity and plasticity
(the ability to change phenotype), resulting in an average survival time of around
15 months after diagnosis.

One potential way to mathematically describe the phenotypic cell development of
glioblastoma is the use of a macroscopic approach via partial integro-differential
equations (PIDEs), whose parameters are fitted based on biological cell data
(e.g. RNA-seq data).

In this seminar, first I will briefly outline the central concepts of the biological
background needed to tackle a mathematical explanation of the phenotypic landscape of
glioblastoma, together with different strategies that could be useful for processing
existing datasets and dealing with their high dimensionality and incompleteness.
The talk will then focus on a potentially practical PIDE-model and the associated
parameter identification task.
Finally, the first partial results of the parameter identification on real patient
data in UMAP representation will be presented for a simplified model.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

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

Program

13:00-15:00 Scientific Short Presentations – SciComp (5+5 minutes each)
(Dr. E. Özkaya, Dr. M. Sagebaum, T. Kortus, O. Burghardt, R. Pochampalli, J. Blühdorn, G. Suarez, Dr. L. Chen, L. Fischer, J. Rottmayer, M. Ngowa Msinda, Dr. A. Linke)

15:00-15:15 Coffee break

Keynote Talks
15:15-16:15 Prof. J. Kieseler (KIT)
16:15-17:15 Prof. N. Beishuizen (TU Eindhoven / Bosch)

17:15-18:00 Scientific Short Presentations – Guests (10+5 minutes each)
(M. Padmanabha (ITWM), M. Klostermeier (DFKI), C. Battistoni (MTU))

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

18:30 Workshop Dinner

Prof. Dr. Jan Kieseler, Institute of Experimental Particle Physics (ETP), Karlsruhe Institute of Technology (KIT)

Title: From Detector Signals to Physics with Machine Learning

Abstract:

Large-scale particle detectors at CERN’s Large Hadron Collider (LHC) and its upcoming high-luminosity upgrade (HL-LHC) record millions of collisions per second, producing sparse, irregular, high-dimensional sensor data from conceptually very different sub-detector systems, such as tracking and calorimetry. Future collider concepts push granularity requirements even further: they aim at unprecedented measurement precision and, despite operating at lower particle densities, place new demands on particle detection (reconstruction) algorithms. Meeting the physics goals of these experiments—precision measurements and searches for extremely rare processes—requires algorithms that can reliably extract thousands of overlapping particles under tight performance and computing constraints, and translate robustly to ultimate-precision detectors.

This motivates a shift away from classical, hand-crafted reconstruction methods—still ubiquitous in modern detectors—toward machine-learning approaches that respect detector geometry, enforce locality, and adapt to varying particle densities. Such models must learn what is physically resolvable by the detector, avoid global operations that hinder robustness, and perform early information compression to keep inference scalable on heterogeneous hardware. At the same time, improved truth definitions and geometry-aware target formulations are essential to achieve stable generalisation across detector configurations and physics conditions.

This talk will outline these requirements from a physics perspective and discuss corresponding machine-learning strategies—locality-preserving architectures, generic simulation benchmarks, and scalable inference schemes—that can sustainably meet the demands of next-generation detectors while also improving reconstruction quality in existing ones.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

Prof. Dr. Nijso Beishuizen, Department of Mechanical Engineering, Eindhoven University of Technology, and BOSCH Deventer

Title: Combustion in SU2: Overview, work in progress and challenges ahead

Abstract:

Combustion in the heat and power industry is in a transition from using traditional fossil fuels to decarbonized fuels like hydrogen.

In this talk we will give an overview of the current combustion capabilities and activities in SU2. We briefly show the different combustion models that are currently available and their performance, specifically the laminar flamelet models and the turbulent flamespeed closure models that can be implemented through user defined transport equations. We will also mention some work in progress and plans for the future like turbulent flamelet models and thermoacoustics.
We will point out some improvements, specifically on the robustness of the SU2 solver and some work in progress on improving the geometric multigrid method.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

Joshua Kelly, University of Liverpool

Title: On the implementation of the discrete adjoint method for the optimisation of multi-row turbomachines in a generic multiphysics context

Abstract:

Modern engineering design is often characterised by complex, multidisciplinary design problems. Such problems have large design spaces which can be difficult to explore. Computational methods have become commonplace in both industry and academia, yet the challenge of improving a design remains difficult and often success depends on designer experience. To this end, one prospective method is the discrete adjoint method for shape optimisation.

The application of the discrete adjoint has been widely used in many applications, however for turbomachinery applications there are still obstacles to overcome. Several authors have implemented multi-row methods using mixing plane approaches with the adjoint capabilities however the implementations are limited by their complexity, unsuitability for multiphysics frameworks and large errors still present despite the use of automatic differentiation. The implementation of a fully turbulent, discrete adjoint mixing plane implementation was undertaken previously in SU2 but recent developments of a generic framework for multiphysics optimisation problems left the implementation incompatible with modern multizone techniques.

In this talk I will present the status of the work on multi-row turbomachinery discrete adjoints in SU2, along with detailing some of the challenges faced in implementation and the solutions used to overcome them. In particular, a discussion on the implementation of a mixing-plane approach which is compatible with the multiphysics discrete adjoint framework available in SU2 will be presented. This work is the result of collaboration between researchers at the University of Liverpool, UK and RPTU.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1

Dr. André Gustavo Carlon, Chair of Mathematics for Uncertainty Quantification, RWTH Aachen University

Title: Bayesian optimal experimental design and its applications in engineering

Abstract:

Bayesian optimal experimental design (OED) seeks to optimize data acquisition by maximizing the expected information gain (EIG). In nonlinear problems, however, estimating and optimizing the EIG is computationally demanding, often requiring a prohibitively large number of model evaluations. In this talk, I show how Laplace-based approximations can be used to make Bayesian OED tractable in challenging engineering applications.

First, I present a stochastic gradient descent (SGD) approach in which the Laplace approximation is used to obtain noisy but inexpensive gradient estimates of the EIG. The robustness of SGD to noise enables efficient optimization of experimental designs using coarse EIG approximations. I demonstrate the method in an electrical impedance tomography experiment aimed at identifying ply orientation angles in composite laminate materials.

In the second application, I consider a source localization problem using unmanned aerial vehicles (UAVs), where the goal is to identify the source of a pollutant from concentration measurements. The optimal UAV path is obtained by solving a mixed discrete–continuous stochastic optimal control problem governed by a Hamilton–Jacobi–Bellman equation, with a value function defined in terms of the EIG. To address the high dimensionality of the resulting PDE, I again employ a Laplace approximation of the EIG.

How to join online

You can join online via Zoom, using the following link:
https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1