  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.
