The field of engineering is witnessing a significant shift towards the adoption of large language models (LLMs) for various applications. Researchers are exploring the potential of LLMs to automate tasks such as model generation, simulation, and optimization. The use of LLMs is enabling engineers to describe complex systems and processes in natural language, which can then be translated into formal models and simulations. This approach has the potential to simplify the modeling process, reduce the need for expertise in specific modeling languages, and increase the efficiency of engineering design and analysis. Notable papers in this area include: ModiGen, which proposes a workflow for multi-task Modelica code generation using LLMs, and LLM-enabled Instance Model Generation, which explores the generation of instance models using LLMs. The Power of Small LLMs in Geometry Generation for Physical Simulations also demonstrates the potential of fine-tuned small LLMs to outperform larger models in specialized tasks.