From what I have seen "world models" are more like video models. The idea is: from a video clip, predict the next frame, like LLMs predict the next word/token from a bit of text.
The idea is that these models should be able to internalize the laws of physics and properties of objects just like LLMs do with grammar rules. To make training more efficient, frames from game engines with hardcoded physics are used. The application is mostly robotics, including autonomous vehicles.
This is a bit different, the idea is to write a physics engine, it means numbers: like for an airplane model, calculate the climb rate. The application is mostly science and engineering.
"world model" is, sadly, a new buzzword; most of the world models we are seeing are video generative models (like GPT-Image), they receive an action vector and previous frames and must generate the next frame. It's working of course.
The whole field of world models also have lighter approaches based on using latent representations as the target (and not pixel frames). LeWorldModel[0] and DINO-WM[1] are a good way to learn how these works.
Strangely, most of the startups in the space of world models are focused on video-generative world models.
A good application of world models is in robotics where an action policy generate a distribution of actions, you can then predict the future state of the world and use some reward policies to choose the "best" future state.