  Several machine learning methods aim to learn or reason about complex
physical systems. A common first-step towards reasoning is to infer system
parameters from observations of its behavior. In this paper, we investigate the
performance of Large Language Models (LLMs) at performing parameter inference
in the context of physical systems. Our experiments suggest that they are not
inherently suited to this task, even for simple systems. We propose a promising
direction of exploration, which involves the use of physical simulators to
augment the context of LLMs. We assess and compare the performance of different
LLMs on a simple example with and without access to physical simulation.
