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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 Thesis49 41 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Identification of hybrid systems with dynamics-based modeling through symbolic regression(Elsevier, 2026-01); ; ; ; Hybrid systems combine both continuous and discrete behavior. These systems serve as models in many fields, including control systems, robotics, and industrial processes. However, due to their complexity, finding an accurate model is a challenge. This paper presents a holistic approach to learning models of hybrid systems using symbolic regression. Our method leverages symbolic regression to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. An advantage of our algorithm is that it detects transitions between different behavioral modes of a system based on the inherent dynamics. From learned expressions for the dynamical behavior of a system, we form a hybrid system by combining the learned expressions with a decision tree determining the current behavioral mode from data. This hybrid decision tree serves regression, prediction, and further related tasks. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real hybrid system and predict output signals on new input data with high accuracy.Publicationtype: Journal Article16 49 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , One-shot learning in hybrid system identification: a new modular paradigm(2025-10-11); ; ; Identification of hybrid systems requires learning models that capture both discrete transitions and continuous dynamics from observational data. Traditional approaches follow a stepwise process, separating trace segmentation and mode-specific regression, which often leads to inconsistencies due to unmodeled interdependencies. In this paper, we propose a new iterative learning paradigm that jointly optimizes segmentation and flow function identification. The method incrementally constructs a hybrid model by evaluating and expanding candidate flow functions over observed traces, introducing new modes only when existing ones fail to explain the data. The approach is modular and agnostic to the choice of the regression technique, allowing the identification of hybrid systems with varying levels of complexity. Empirical results on benchmark examples demonstrate that the proposed method produces more compact models compared to traditional techniques, while supporting flexible integration of different regression methods. By favoring fewer, more generalizable modes, the resulting models are not only likely to reduce complexity but also simplify diagnostic reasoning, improve fault isolation, and enhance robustness by avoiding overfitting to spurious mode changes.Publicationtype: Conference Paper33 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Usability of Symbolic Regression for Hybrid System Identification - System Classes and Parameters(2024-11-26); ; ; 13 18 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , FaMoS– Fast Model Learning for Hybrid Cyber-Physical Systems using Decision Trees(Association for Computing Machinery (ACM), 2024-05-14); ; ; In the domain of cyber-physical systems, there is an increasing relevance of data-driven approaches for the learning of hybrid system dynamics. In particular, accurate models have been successfully abstracted from continuous (real-valued) traces and applied for various goals. However, industrial applications involving online modeling or rapid prototyping have two additional requirements: 1) runtime efficiency and 2) the interpretability of the approach and results. This work adopts a common break down of this learning problem into four steps: 1) trace segmentation, 2) segment clustering, 3) characterization of the dynamics for each cluster (mode) and 4) learning of the overall model of mode transitions. Correspondingly, the bottlenecks in the state-of-the-art approaches are identified and discussed. Then, in a heuristic manner, interpretable and time-efficient algorithms for each of the steps are proposed giving a novel approach named FaMoS. The accuracy and runtime efficiency of the approach are evaluated for several system examples. FaMoS shows very short learning time, while the model’s predictions of system dynamics are close to the ground truth behavior.Publicationtype: Conference Paper46 98 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Automata learning for automated test generation of real time localization systems(2021-05-21); ; ; ; Cyber Physical Systems (CPSs) are often black box systems for which no exact model exists. Automata learning allows to build abstract models of CPSs and is used in several scenarios, i.e. simulation, monitoring, and test case generation. Real time localization systems (RTLSs) are an example of particularly complex and often safety critical CPSs. We present a procedure for automatic test case generation with automata learning and apply this approach in a case study to a localization system.Publicationtype: Conference Paper59 25