CORE group investigates methods and concepts to assess the stability of highly dynamic systems and define strategies to manage the underlying complexity.
The continuous development process of artificial systems (e.g., software, critical infrastructure) and their interactions with their environments leads to a constant increase in complexity on various levels. Due to the diversity and opacity of interactions both within and outside these systems, it is increasingly challenging to fully anticipate adverse incidents and manage their effects. Therefore, the primary objective of the CORE group is to develop novel analytical approaches for assessing the complexity and resilience characteristics of man-made systems and developing strategies to enhance its performance.
Within a highly interdisciplinary team, our research topics include (but are not limited to):
Techniques for advancing machine learning capabilities, including preprocessing, processing, and post-processing.
Enhancing transparency, interpretability, and explainability of Artificial Intelligence and Digital Twins
Studying the underlying rules of natural and man-made systems to understand behaviors ranging from nonlinearity to multilayered interactions between different components. By deducing mechanisms that regulate biological systems, we provide new insights into assessing software aging and maintenance.
Investigating and advancing methodological approaches in Information Theory and Complexity Science for multivariate time series analysis and complex matrices. The goal is to gain insights into the dynamics of various systems and to study underlying patterns for identifying errors, security risks, early warning signals, and enhance prediction accuracy of Machine Learning tasks.
Exploring quantum-inspired machine learning methods for identifying abnormal system behavior in network structures and reducing functional risk.
The Complexity and Resilience Group participates in the following research projects:
