Foundation models are trained on a large amount of data to learn generic patterns. Consequently, these models can be used and fine-tuned for various purposes. Naturally, studying such models' use in the context of digital twins for cyber-physical systems (CPSs) is a relevant area of investigation. To this end, we provide perspectives on various aspects within the context of developing digital twins for CPSs, where foundation models can be used to increase the efficiency of creating digital twins, improve the effectiveness of the capabilities they provide, and used as specialized fine-tuned foundation models acting as digital twins themselves. We also discuss challenges in using foundation models in a more generic context. We use the case of an autonomous driving system as a representative CPS to give examples. Finally, we provide discussions and open research directions that we believe are valuable for the digital twin community.

翻译：基础模型通过海量数据训练学习通用模式，因此可针对不同任务进行微调与部署。研究此类模型在信息物理系统数字孪生领域的应用具有重要价值。本文从CPS数字孪生开发的多维度视角出发，探讨基础模型如何提升数字孪生构建效率、增强其功能效能，并可作为经专业化微调的基础模型直接充当数字孪生体。同时分析了基础模型在泛化应用场景中面临的挑战，以自动驾驶系统作为典型CPS案例进行实证说明。最后提出对数字孪生领域具有参考价值的讨论议题与开放式研究方向。