Special Session Chairs:
- Dr. Sándor HAJDU | University of Debrecen, Hungary
- Dr. Csongor Márk HORVÁTH | Norwegian Institute of Bioeconomy Research, Norway
Scope:
This special track focuses on emerging paradigms in AI science, with particular emphasis on AI optimization and on improving the precision, explainability, and provability of AI systems. It aims to advance fundamental mathematical frameworks that provide provable performance guarantees for AI-driven control solutions, moving beyond conventional experience-based and heuristic learning approaches. In the domain of Explainable AI, the track especially encourages contributions that develop linguistic interpreters of AI “thinking,” enhancing the transparency and interpretability of reasoning and decision-making processes.
The session is strongly application-oriented and seeks to introduce tensor product-based polytopic modeling and control methods into new and previously unexplored engineering domains. The track is structured around three main pillars: (i) theoretical foundations, (ii) engineering applications, and (iii) psychological applications.
Topics of interest include, but are not limited to, tensor product-based approaches to:
- control of LPV systems
- disturbance observer design
- condition monitoring of engineering systems
- electrical energy production
- energy distribution and power electronics network pollution
- modeling of environmental effects, including noise and other forms of environmental pollution