Modellansatz - English episodes only

This episode was recorded in March 2026. Gudrun speaks again with Nadja Klein and Moussa Kassem Sbeyti who work at the Scientific Computing Center (SCC) at KIT in Karlsruhe. As a new person in our conversation we welcome Nicolas Bianco. The research of the scientists in Nadja's MBD Lab is at the intersection of statistics and machine learning. It spans theoretical analysis, method development and real-world applications. Last time we focussed on Baysian statistics. With the help of Nicolas we want to examplify how interdisciplinary work is done and how his journey led him into this field of research. Since in this episode we very much focussed on Nicolas decision process and steps in his carrier we plan to have an episode on the topics later in the year. Literature and further information Webpage of the group Webpage of Moussa Kassem Sbeyti Webpage of Nicolas Bianco M. K. Sbeyti and N. Klein: Depth as prior knowledge for object detection, arXiv:2602.05730, 2026 Podcasts Ep 42 of Vanishing Gradient, Jan 2025. O. Beige, G. Thäter: Risikoentscheidungsprozesse, Gespräch im Modellansatz Podcast, Folge 193, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2019. N. Klein, M. Sbeyti, G. Thaeter: Bayesian Learning, Gespräch im Modellansatz Podcast, Folge 253, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2025.

Gudrun had two podcast conversations at the FEniCS18 workshop in Oxford (21.-23. March 2018). FEniCS is an open source computing platform for solving partial differential equations with Finite Element methods. This is the first of the two episodes from Oxford in 2018. Roisin Hill works at the National University of Ireland in Galway on the west coast of Ireland. The university has 19.000 students and 2.000 staff. Roisin is a PhD student in Numerical Analysis at the School of Mathematics, Statistics & Applied Mathematics. Gudrun met her at her poster about Balanced norms and mesh generation for singularly perturbed reaction-diffusion problems. This is a collaboration with Niall Madden who is her supervisor in Galway. The name of the poster refers to three topics which are interlinked in their research. Firstly, water flow is modelled as a singularly perturbed equation in a one-dimensional channel. Due to the fact that at the fluid does not move at the boundary there has to be a boundary layer in which the flow properties change. This might occur very rapidly. So, the second topic is that depending on the boundary layer the problem is singularly perturbed and in the limit it is even ill-posed. When solving this equation numerically, it would be best, to have a fine mesh at places where the error is large. Roisin uses a posteriori information to see where the largest errors occur and changes the mesh accordingly. To choose the best norm for errors is the third topic in the mix and strongly depends on the type of singularity. More precisely as their prototypical test case they look for u(x) as the numerical solution of the problem for given functions b(x) and f(x). It is singularly perturbed in the sense that the positive real parameter ε may be arbitrarily small. If we formally set ε = 0, then it is ill-posed. The numercial schemes of choice are finite element methods - implemented in FEniCS with linear and quadratic elements. The numerical solution and its generalisations to higher-dimensional problems, and to the closely related convection-diffusion problem, presents numerous mathematical and computational challenges, particularly as ε → 0. The development of algorithms for robust solution is the subject of intense mathematical investigation. Here “robust” means two things: The algorithm should yield a “reasonable” solution for all ranges of ε, including resolving any layers present; The mathematical analysis of the method should be valid for all ranges of ε. In order to measure the error, the energy norm sounds like a good basis - but as ε^2 → 0 the norm → 0 with order ε . They were looking for an alternative which they found in the literature as the so-called balanced norm. That remains O(1) as ε → 0. Therefore, it turns out that the balanced norm is indeed a better basis for error measurement.After she finished school Roisin became an accountant. She believed what she was told: if you are good at mathematics, accountancy is the right career. Later her daughter became ill and had to be partially schooled at home. This was the moment when Roisin first encountered applied mathematics and fell in love with the topic. Inspired by her daughter - who did a degree in general science specialising in applied mathematics - Roisin studied mathematics and is a PhD student now (since Sept. 2017). Her enthusiasm has created impressive results: She won a prestigious Postgraduate Scholarship from the Irish Research Council for her four year PhD program. References R. Lin, M. Stynes: A balanced finite element method for singularly perturbed reaction diffusion problems. SIAM J. Numer. Anal., 50(5):2729–2743, 2012. T. Linß: Layer-adapted meshes for reaction-convection-diffusion problems, volume 1985 of Lecture Notes in Mathematics. Springer-Verlag, Berlin, 2010. H.-G. Roos, M. Stynes, L. Tobiska: Robust Numerical Methods for Singularly Perturbed Differential Equations, volume 24 of Springer Series in Computational Mathematics. Springer-Verlag, Berlin, 2nd edition, 2008. Podcasts M. E. Rognes: Cerebral Fluid Flow, Gespräch mit G. Thäter im Modellansatz Podcast, Folge 134, Fakultät für Mathematik, Karlsruher Institut für Technologie (KIT), 2017.