The advancements of mixed reality services, with the evolution of network
virtualization and native artificial intelligence (AI) paradigms, have
conceptualized the vision of future wireless networks as a comprehensive entity
operating in whole over a digital platform, with smart interaction with the
physical domain, paving the way for the blooming of the Digital Twin (DT)
concept. The recent interest in the DT networks is fueled by the emergence of
novel wireless technologies and use-cases, that exacerbate the level of
complexity to orchestrate the network and to manage its resources. Driven by
the internet-of-sensing and AI, the key principle of the DT is to create a
virtual twin for the physical entities and network dynamics, where the virtual
twin will be leveraged to generate synthetic data, in addition to the received
sensed data from the physical twin in an on-demand manner. The available data
at the twin will be the foundation for AI models training and intelligent
inference process. Despite the common understanding that AI is the seed for DT,
we anticipate the DT and AI will be enablers for each other, in a way that
overcome their limitations and complement each other benefits. In this article,
we dig into the fundamentals of DT, where we reveal the role of DT in unifying
model-driven and data-driven approaches, and explore the opportunities offered
by DT in order to achieve the optimistic vision of 6G networks. We further
unfold the essential role of the theoretical underpinnings in unlocking further
opportunities by AI, and hence, we unveil their pivotal impact on the
realization of reliable, efficient, and low-latency DT. Finally, we identify
the limitations of AI-DT and overview potential future research directions, to
open the floor for further exploration in AI for DT and DT for AI.

数字孪生网络在网络编排、资源管理和人工智能模型训练及智能推理等方面具有重要作用，是实现 6G 网络的关键技术之一。本文探讨数字孪生网络与人工智能相互促进的机制，并为探索数字孪生网络与人工智能的未来研究方向提供参考。