  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.
