New Topical Programme “Artificial Intelligence and Complexity” strengthens research at the interface of artificial intelligence and complexity

The Rectorate of the University of Münster has included the joint project “Artificial Intelligence and Complexity” in its strategic funding line Topical Programmes. The project is led by Dr Oliver Kamps (Center for Data Science and Complexity (CDSC), Managing Director and Scientific Coordination) and Prof. Dr Uwe Thiele (Institute for Theoretical Physics, Spokesperson of the CDSC Executive Board). The programme will consolidate interdisciplinary research at the University of Münster in the field of data-driven methods and complex systems and is anchored at the Center for Data Science and Complexity (CDSC).

The aim of the project is to systematically integrate artificial intelligence into experiment, simulation and theory – particularly in research areas where classical approaches reach their limits. At its core is the mutual reinforcement of AI and complexity research: insights from the theory of complex systems inspire new, interpretable and robust machine learning methods, while AI-based approaches enable new ways of analysing, modelling and predicting complex dynamic phenomena such as tipping points, phase transitions and emergent collective behaviour.

The project addresses complex systems across a wide range of scientifically and societally relevant contexts, including biological and neural networks, intelligent materials, epidemiological and climate systems, energy networks and social dynamics. By combining data-driven methods with domain knowledge – for example through physics-informed machine learning or model-based system identification – the programme aims to drive methodological innovation with broad applicability across disciplines.

A central structural element of the project is the development of a digital, interdisciplinary knowledge base. This resource will systematically curate key concepts, methods and data types from the participating disciplines in order to facilitate entry into interdisciplinary research. Interactive elements such as visualisations, glossaries and executable examples will support collaboration, accelerate scientific onboarding and promote coherent education across disciplinary boundaries.