On closer inspection, we find science and especially mathematics throughout our everyday lives, from the tap to automatic speed regulation on motorways, in medical technology or on our mobile phone. What the researchers, graduates and academic teachers in Karlsruhe puzzle about, you experience firsthand in our podcast "The modeling approach".
Modellansatz - English episodes only
Claim This Podcastby Gudrun Thäter, Sebastian Ritterbusch
47 episodes
Updated Daily
Accepts GuestsHas SponsorsLocation 🇩🇪
21
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality42
Social0
YouTube0
Engagement0
Podcast Overview
On closer inspection, we find science and especially mathematics throughout our everyday lives, from the tap to automatic speed regulation on motorways, in medical technology or on our mobile phone. What the researchers, graduates and academic teachers in Karlsruhe puzzle about, you experience firsthand in our podcast "The modeling approach".
Language
🇺🇲
Publishing Since
5/7/2015
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21
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality42
Social0
YouTube0
Engagement0
7
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10
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excellent
Episode Length
41 minutes
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3.0/5
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Every 83 days
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Recent Episodes
June 19, 2026
Fractional Laplacian
Gudrun talks with Debajyoti Choudhuri. He is staying at KIT as a short term guest. He is Associate Professor in the School of Basic Sciences at IIT Bhubaneswar, India. He did his M.Sc. and Ph.D. in Mathematics at the University of Hyderabad. His research interest lies in the analysis of elliptic PDEs using Functional Analytic and topological methods. In this he touches and has a slight overlap with the research of Gudrun. The conversation starts with the discussion about a small paper which Debajyoti put on the archiv. It is about understanding how to work with the Fractional Laplacian. This means extending the classical Laplace operator Δ to non-integer powers. This operator is the main part in PDEs which model, e.g, anomalous diffusion, probability theory, image processing, finance, and nonlocal mechanics. (-Δ)s, where s is in (0,1). What makes It different to the ordinary Laplacian? While the traditional Laplace operator is local, i.e. it depends only on values of u and its derivatives near x, the fractional Laplacian is nonlocal, it depends on values of u everywhere in space. Thus, for the analytical and numerical treatment one needs very different methods. There are several possible definitions. Some of them can be found in the Wikipedia article which is cited below. On ℝn, the cleanest definition is the Fourier definition which follows the idea: Take the Fourier transform. Multiply by |ξ|2s. Transform back. In the short paper which is discussed the singular integral definition is used: For s in (0,1): (-Δ)^s u(x) = C(n,s) PV ∫ [u(x) - u(y)] / |x - y|^(n + 2s) dy This makes the nonlocality explicit: every point y contributes to the value at x. The method central in studying Laplace problems is variational. It considers an (infinite) family of generalised problems and works on the existence of so-called weak solutions. These problems are formulated with the help of . The weak solution for the Laplace problem is an element of the space H1=W1,2. This means the solution and its (generalised) gradient are bounded in L2 in the domain in which the problem is solved. This has physical meaning and due to known properties (embedding) of Sobolev spaces the pointwise (strong) solutions often can be constructed when enough regularitiy of the weak solutions is proved. Fractional Laplacians naturally live in fractional Sobolev spaces. These are not that easy to connect to physical properties and a few of the equivalent definitions in the context of classical Sobolev spaces are not equivalent any more everywhere. Common approaches for numerics for PDEs including the fractional Laplacian are: Fourier spectral methods (periodic domains), Finite element methods for fractional PDEs, Matrix-function methods (As), Caffarelli–Silvestre extension methods, Quadrature approximations of singular integrals. The Extension trick introduced by Caffarelli and Silvestre in 2007 (their original paper is cited below) is also discussed as part of the short note. p-laplacian augurs well in the sense because the unicity of the definitions of the s-laplacian is still lacking. The conversation then turns to how Debajyoti found his way into mathematics and the topic of PDEs and how life and work feel like in his university.
June 7, 2026
LLM statistics
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.
May 2, 2025
Bayesian Learning
Host Gudrun interviews Nadja Klein and Moussa Kassem Sbeyti about their research at the intersection of statistics and machine learning, specifically focusing on Bayesian methods to improve machine learning models.
47 total episodes available
Recent guests on Modellansatz - English episodes only
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Nadja Klein
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Moussa Kassem Sbeyti
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Polyxeni Spilioti
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Stephanie Anne Salomone
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Marta Betcke
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Anne-Sophie Bonnet-BenDhia
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Mariana Haragus
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Fioralba Cakoni
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Anna Geyer
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Changjing Zhuge
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Serena Carelli
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Magdalena Gonciarz
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