Plenary Talks
Prof. JÜRGEN KURTHS
Institute of Physics, Humboldt-Universität zu Berlin, Berlin, Germany
Abstract
The infrastructure, in particular power grids, of our modern society is efficient but also sensitive concerning strong perturbations, as attacks on the cybersystem or extreme climate events. Power grids are strongly ongoing a transition to distributed renewable energy sources. The desynchronization of a few of those would likely result in a substantial blackout. Thus, the dynamical stability of the wanted synchronous state has become a leading topic in power grid research, in particular for rather strong perturbations where traditional linearization-based concepts are not appropriate. To treat this problem, we discuss the concept of basin stability covering strong perturbations and identify most vulnerable motifs in power grids. Considering the vulnerability of power grids against extreme wind loads and, consequently, increasing its robustness to withstand these events is of great importance. Here, we combine adetailed model of the climatic drivers of extreme events, and a cascadable model of the transmission network to provide a holistic co-evolution model to explore wind-induced failures of transmission lines in the Texan electrical network. The proposed modelling approach could be a tool so far missing to effectively strengthen power grids against future hurricane or typhoon risks even under limited knowledge.
Biography
Jürgen Kurths studied mathematics at the University of Rostock and received his PhD. degree from the GDR Academy of Sciences. He worked as full Professor at the University of Potsdam, and since 2008 he joined the Humboldt University; at the same time, he possesses the Chair of the Research Department 4 “Complexity Science” of the Potsdam Institute for Climate Impact Research. His primary research interests include synchronization, complex networks, and time series analysis and their applications in Earth Sciences, Physiology, engineering, and others. He has made major contributions in nonlinear dynamics theory and climate networks. Prof. Kurths has published more than 600 articles that enjoying enormous citation (~120000, h-ind. 144) and has been a highly cited researcher since 2017. He is a Fellow of the American Physical Society, the Royal Society of Edinburgh and of the Network Science Society, and a member of the Academia Europaea. He received an Alexander von Humboldt Research Award in 2005 and 2021, the Richardson award from the European Geoscience Union in 2013, the Lagrange Award of Nonlinear Science and Complexity Conferences in 2022 and the SigmaPhi Price of the European Physical Society (together with Michael Kosterlitz, Nobel Price 2016) in 2023. He received eight Honorary Doctorates and Honorary Professorships. Finally, Prof. Kurths is Editor-in-chief of CHAOS – A Journal of Nonlinear Science.
Prof. Klaus Mainzer
President of the European Academy of Sciences and Arts, Salzburg, Austria. TUM Senior Excellence Faculty, Technische Universität München, Munich, Germany. Carl Friedrich von Weizsäcker-Center, Eberhard-Karls-Universität Tübingen, Germany
Abstract
According to several prominent authors, a main part of 21st century science will be on complexity research. The intuitive idea is that global patterns and structures emerge from locally interacting elements like atoms in laser beams, molecules in chemical reactions, proteins in cells, cells in organs, neurons in brains, etc. (Mainzer 2007). Complex pattern formation has been reported from many disciplines (e.g., physics, chemistry, biology, brain research). The causes of complex pattern formation have been analyzed from various perspectives such as, e.g., Schrödinger's order from disorder (1948), Prigogine's dissipative structure (1980), Haken's synergetics (1983), Langton's edge of chaos (1990), brain research (Hodgkin/Huxley 1952), and memristive cellular networks (Mainzer/Chua 2013).
Recently, these results of complexity research have become important for machine learning of AI systems. Instead of complex pattern formation in nature, complex pattern recognition of artificial neural networks (ANN) is considered which is modeled in statistical learning theory. But ANN-systems are only simulated on digital computers with von-Neumann architectures which run into severe problems of energy consumption (von-Neumann bottleneck) in the future - in contrast to the highly energetically efficient biological brains. Therefore, neuromorphic systems with more brain-orientated computing such as memristive and photonic networks should be considered also for hardware realization. The research target is not only sustainable computing and AI with respect to energy and environment, but also to overcome mathematical limits of digital computing (Mainzer 2024).
References: K. Mainzer, Thinking in Complexity, Springer: Berlin 5th edition 2007; K. Mainzer/L. Chua, Local Activity Principle. The Cause of Complexity, World Scientific Singapore 2013; K. Mainzer, Artificial Intelligence. When do Machines take over? Springer: Berlin 2nd edition 2019; K. Mainzer, Artificial Intelligence of Neuromorphic Systems. From Digital, Analogue, Quantum, and Brain-Orientated Computing to Hybrid AI, World Scientific Singapore 2024.
Biography
Klaus Mainzer works as a philosopher of science on the foundations and future perspectives of science and technology. His focus is on the mathematization and computer modeling of science and technology. He became known as a complexity researcher who primarily investigates complex systems in nature, technology, business and society, the foundations of artificial intelligence and big data. Starting with constructive and algorithmically based mathematical methods, which were the subject of his doctorate, he dealt with their applications in geometry and physics in his habilitation. This resulted in the first books on the foundations and history of geometry, the concept of number (with H. Hermes, F. Hirzebruch, R. Remmert and others), the concept of symmetry and time, the theory of relativity, quantum mechanics and cosmology (with J. Audretsch) between 1980 and 1990. Since the beginning of the 1990s, computer-aided modeling of complex systems and their nonlinear dynamics has taken center stage. Numerous publications have been published on complex systems (with H. Haken), cellular automata and neural networks (with L. Chua). The starting point of Klaus Mainzer's research since his dissertation has been the question of the computability of the world. We live in a data-driven age of increasing complexity, the development of which is accelerated by exponential growth laws of data volumes, computer and storage capacities in global networks. The aim of his research is therefore constructively based solution and proof procedures in mathematics (with H. Schwichtenberg et al.) with which the algorithmization and digitization of technology and society remain controllable. He also devotes himself to this social goal in numerous publications, public lectures and contributions to the media. He is member of various academies all over the world. He currently serves as President of the European Academy of Sciences and Arts.
