Confirmed plenary speakers:

Prof. Dr. Franziska Klügl

Örebro University (Sweden), Homepage

If Agent-Based Simulation is the hammer, what is the nail?

For more than 20 years, Agent-Based Simulation (ABS) has been used to model and simulate complex systems consisting of many interacting actors. Over the years, it has become a prominent approach in many domains beyond social sciences. Yet, especially during and after Covid, we - who are using and promoting ABS in different contexts - have to observe that we may have fooled ourselves to some degree about the actual relevance of ABS in practice. Did we miss something?
In this presentation, I will introduce ABS with the possibilities that its usage provides, analyze the challenges that researchers and practitioners using ABS still face, and discuss potential solutions to those challenges. Finally, the presentation will address the question in the title - what characterizes the systems that are best "nailed" by ABS.

Prof. Dr. Alexander Hartmaier

Ruhr-Universität Bochum (Germany), Homepage

Towards a digital material twin: Data-oriented microstructure-property relationships

The possibilities of using machine learning models to support or even replace traditional models in solid mechanics have inspired a plethora of new ideas and research directions. For example, in data-driven mechanics, completely new formulations of mechanical equilibrium under non-linear material responses have been introduced. Other works are seeking to replace constitutive models, which are commonly formulated in terms of closed-form algebraic equations or as ordinary differential equations, by trained machine learning algorithms. Such a trained machine learning model can be considered as a digital material twin as it describes the specific material performance and can be used to adapt processing conditions on-line or to predict the remaining lifetime of a component. It is demonstrated that machine learning models can be successfully trained with history-dependent mechanical data for polycrystals with different crystallographic textures. The required training data is obtained from micromechanical simulations based on the crystal plasticity method. The trained model accurately describes the texture-specific elastic-plastic material response under multiaxial loading conditions. In future work, the dynamics of microstructure evolution under thermal and mechanical loads will be included, to fully integrate the process-microstructure-property conditions in the digital material twin.

Prof. Dr. Piotr Faliszewski

AGH University of Science and Technology (Poland), Homepage

Map of Elections

In this talk I will present the idea of a map of elections. Briefly put, the map is a tool for visualizing relations between election instances, where each instance consists of a set of candidates and a set of votes over these candidates. The map also includes a distance measure between elections---invariant to renaming the candidates and reordering the votes---and arranges the elections in 2D space in a way that maintains---as much as possible---that the Euclidean distances implied by this embedding resemble the distance measure. We will describe several ways of generating such a map and show a number of maps' applications. At the end, we will briefly mention maps of several other types of objects.