Christian Vergara-Marcillo

h-index2
2papers
12citations

2 Papers

9.3SEJun 22
Integrating Heterogeneous Digital Twins in Federated Ecosystems

Christian Vergara-Marcillo, Rami Bahsoon, Nikos Tziritas et al.

Digital Twins (DTs) are increasingly used to virtualise physical systems at different scales, enabling monitoring, simulation, and predictions to support decision-making. However, while individual DTs are effective in stand-alone settings, ecosystem-scale deployments require multiple autonomous and distributed DTs to cooperate across system boundaries despite differences in modelling approaches or software technologies, making interoperability and runtime coordination critical challenges. Although \textit{Federated Digital Twin Ecosystems} have emerged as a promising direction, existing research remains at the conceptual stage, offering high-level architectures while leaving the practical integration of heterogeneous DTs underexplored. This paper proposes the \textit{Federation Node Manager}, a modular integration mechanism that connects local DTs to a federated environment through controlled capability exposure, protocol and schema adaptation, and timely state and event exchange for coordinated operations. We present a conceptual design and a prototype implementation, and demonstrate their feasibility in the smart mobility domain for emergency response scenarios. The proposed mechanism serves as an enabling component within a broader service-oriented federated DT ecosystem.

8.5AIMay 23, 2024Code
Large Language Models for Explainable Decisions in Dynamic Digital Twins

Nan Zhang, Christian Vergara-Marcillo, Georgios Diamantopoulos et al.

Dynamic data-driven Digital Twins (DDTs) can enable informed decision-making and provide an optimisation platform for the underlying system. By leveraging principles of Dynamic Data-Driven Applications Systems (DDDAS), DDTs can formulate computational modalities for feedback loops, model updates and decision-making, including autonomous ones. However, understanding autonomous decision-making often requires technical and domain-specific knowledge. This paper explores using large language models (LLMs) to provide an explainability platform for DDTs, generating natural language explanations of the system's decision-making by leveraging domain-specific knowledge bases. A case study from smart agriculture is presented.