Keynote Talks

Plenary Talks

Mathematical Modeling in Climate Research:
Characteristics and Challenges

Prof. Thomas Slawig
Leader of the research group Algorithmic Optimal Control — CO2-Uptake of the Ocean, Department of Computer Science, Kiel University, Germany

Climate research and climate predictions heavily rely on models. In this talk, we discuss the underlying modeling principles, specific features and characteristics of climate models, and the resulting consequences for practical applications. The climate system and, thus, also climate models are highly complex. Their evaluation on the computer requires a high computational effort. Moreover, the recent discussions about climate change add some important aspects: The human influence on the climate system results in incorporation of economic and social models, in addition to the classical physical, chemical and biological basis. Vice versa, model results might be used to derive guidelines for political, social and economic strategies. Consequently, reliability of model results and their uncertainty are important issues. Overall, climate modeling and simulation is a highly interdisciplinary topic. This gives rise to look also at the different meanings of the notion “model” in mathematics, computer and climate science.

About the speaker
Thomas Slawig is currently Professor at Kiel University, Germany, where he is leading the research group “Algorithmic Optimal Control – CO2-Uptake of the Ocean”. After earning his PhD in Mathematics in 1998, he has been a scientist at the Potsdam-Institute for Climate Impact Researsch (PIK) and TU Berlin, where he received the “Habilitation” in 2005 . He is a Member of the DFG Excellence Cluster Future Ocean and participates in the German National Climate Modeling Initiative “PalMod: Modeling a complete glacial cycle”.

A Mathematical Diesel Engine Model, its Evolution and Impact on Clean and Efficient Marine Transportation

Prof. Lars Eriksson
Professor for Vehikular Systems at Linköping University, Sweden.

Model based development is seen as a key methodology for handling the complexity and guiding the development and optimization of future complex hybrid electric vehicles. It can help reduce the time to market and thus increase the pace of innovation, but a cornerstone for a high innovation pace is the availability and reusability of mathematical models. In this presentation, we will follow the initiation and development of a diesel engine model that has been much used and evolved over the years to become used in a wide range of applications beyond the initial intentions. Starting as a model for a long haulage truck it has been refitted to a passenger car, reused in a diesel electric powertrain in an off-highway application, reused as building blocks for a large marine engine model. Where analysis and adaptive control based on the model has been used to develop clean marine engines fulfilling the UN emission goals. It is now the cornerstone in a benchmark model for development of planning strategies in future connected vehicles as well as in a model for studying hybrid vehicles and how the powertrain interacts with the after-treatment system. Much of the success of the model, builds on the fact that it is component based, systematically developed, and adapted to a real-world engine with documented agreement with measurement data, in addition to that it was released as an open-source model that can be freely downloadable and modified.

About the speaker
Lars Eriksson is Full Professor in Vehicular Systems at Linköping University, Sweden. His research interest is modeling, control, and optimization of clean and efficient vehicle propulsion for sustainable transports. His contributions are foremost on engine control and control-oriented modeling of combustion engines. As the manager of vehicular systems laboratories, he has developed a well-established international network of contacts with research groups both in academia and industry.

Challenges in modelling and detecting the impact of  human aptitudes and preferences in economics and finance

Prof. Marina Dolfin
Professor for Applied Mathematics at the Engineering Department of the University of Messina, Italy.

It is nowadays well-known that the classical deterministic mathematical tools, based on causality principles, generally fail when dealing with the complexity features of behavioral systems. In this talk, I want to address some of these complexity features, i.e. rationality vs. bounded rationality, homogeneity vs. heterogeneity, equilibrium vs. out-of-equilibrium and linearity vs. non-linearity.  I will present a toy model to explore the impact of human altruistic vs. selfish aptitudes on the asymptotic wealth distribution of a simulated simple society, as an example to address the aforementioned features using the tools of the kinetic theory of active particles. Finally, I will change the prospective towards the empirical one, presenting an example on detecting lottery-type preferences of investors using market data from the New York Stock Exchange, by means of capital asset pricing models. In this case the emphasis is mostly placed on the role of computation, discussing some aspects related to classical regression vs. machine learning techniques.

About the speaker
Marina Dolfin is Professor at the Engineering Department of the University of Messina (Italy) in Applied Mathematics. She is a visiting lecturer since the 2017 and currently PhD at the King’s Business School of the King’s College London (UK) in complex systems in economics and finance.


Evening Plenary Lecture

Modelling the “Intangible”:
AI, Machine Learning, and Musical Expressivity

Prof. Gerhard Widmer
Institute of Computational Perception, Johannes Kepler University Linz, and LIT AI Lab, Linz Institute of Technology, Austria

Two decades of scientific work in the field of Music Information Research (MIR) have produced impressive advances in computational modelling of musical abilities: computers can recognise beat, rhythm, harmonies and other musical patterns in acoustic signals; they can classify music by style and genre, learn to predict the taste of human listeners, reliably identify pieces, track and synchronise live performances, and much more, and this has led to a large number of novel applications in the digital music world. But music is (much) more than notes, beats, patterns: music has the power to engage us emotionally, to touch us, it can express, communicate and evoke emotions. This presentation will give the audience an idea of what it means to try to model expressive and emotion-related aspects of music, and teach computers to recognise and predict expressive qualities. Specifically, we will look at recent work on computational models of expressive music performance, and will discuss and demonstrate possible applications of this research. In the process, we will see just how complex music and music perception are, and that there is a need and potential for much more research in the field of computational music modelling.

About the speaker
Gerhard Widmer is Professor and Head of the Institute of Computational Perception at Johannes Kepler University, Linz, Austria, and deputy director of the LIT AI Lab at the Linz Institute of Technology (LIT). His research interests include AI, machine learning, acoustic and music perception, and computational musicology, and his work is published in a wide range of scientific fields, from AI and machine learning to audio, multimedia, musicology, and music psychology. He is a Fellow of the European Association for Artificial Intelligence and has been awarded Austrias highest research awards, the START Prize (1998) and the Wittgenstein Prize (2009), as well as two ERC Advanced Grants (2015, 2021) for research on computational modelling in music.