Date: Fri. September 20, 2024
Event: FAU MoD Lecture
Organized by: FAU MoD, the Research Center for Mathematics of Data at Friedrich-Alexander-Universität Erlangen-Nürnberg (Germany)

FAU MoD Lecture: Thoughts on Machine Learning
Speaker: Prof. Dr. Rupert Klein
Affiliation: Mathematik & Informatik, Freie Universität Berlin (Germany)

Abstract. Techniques of machine learning (ML) find a rapidly increasing range of applications touching upon social, economic, and technological aspects of everyday life. They are also being used with great enthusiasm to fill in gaps in our scientific knowledge by data-based modelling approaches. I have followed these developments for a while with interest, concern, and mounting disappointment. When these technologies are employed to take over decisive functionality in safety-critical applications, we would like to exactly know how to guarantee their compliance with pre-defined guardrails and limitations. Moreover, when they are utilized as building blocks in scientific research, it would violate scientific standards -in my opinion- if these building blocks were used without a throrough understanding of their functionality, including inaccuracies, uncertainties, and other pitfalls. In this context, I will juxtapose (a subset of) deep neural network methods with the family of entropy-optimal Sparse Probabilistic Approximation (sSPA) techniques developed recently by Illia Horenko (RPTU Kaiserslautern-Landau) and colleagues.

BIO.- Rupert Klein holds a doctorate degree (1988) and habilitation (1995) in Mechanical Engineering from RWTH Aachen University with specialization in “Nonstationary Mechanics”. Following his rising interest in environmental science, he accepted an offer to take up a professorship on “Safety Technological” at Bergische Universität Wuppertal in 1995. Two years later, he received a two-legged appointed by Freie Universität Berlin (FUB) and the Potsdam-Institut for Climate Impact Research (PIK) to become the head of the “Data & Computation” Department at PIK (1997-2007) and to establish a research group on “Scientific Computing/Modelling and Simulation of Global Environmental Systems” within the Mathematics & Informatics Department of FUB. His research covers topics of theoretical and computational fluid dynamics in a broad sense, with applications to atmospheric flows, vortex dynamics, and combustion.


This is a hybrid event (On-site/online) open to: Public, Students, Postdocs, Professors, Faculty, Alumni and the scientific community all around the world.


Fri. September 20, 2024 at 14:30H (Berlin time)


On-site / Online

[On-site] Friedrich-Alexander-Universität Erlangen-Nürnberg.
Room H13 Johann-Radon-Hörsaal
Cauerstraße 11, 91058 Erlangen
GPS-Koord. Raum: 49.573764N, 11.030028E

[Online] FAU Zoom link
Meeting ID: 624 1094 3213 | PIN code: 694096

* Photo speaker by Mathematisches Forschungsinstitut Oberwolfach.

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• FAU MoD Lecture: Free boundary regularity for the obstacle problem by Prof. Dr. Alessio Figalli
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• FAU MoD Lecture: From Physics-Informed Machine Learning to Physics-Informed Machine Intelligence: Quo Vadimus? by Prof. Dr. George Karniadakis
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