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Item type:Publication, Access status: with files , Data-driven identification of models for discrete and hybrid systems(2026); This thesis addresses the challenge of identifying interpretable models for cyber-physical systems. Data-driven identification leverages observed data to automatically construct models that capture the underlying behavior of systems. The work explores a progression of methods, ranging from classical automata learning and extensions of decision trees, to novel algorithms for hybrid automata learning. Empirical evaluations demonstrate the effectiveness and complementarity of the proposed methods, showcasing their ability to address a wide range of modeling challenges in discrete and hybrid systems.Publicationtype: Doctoral Thesis47 39