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Going from the parent comment's assertion, the answer would be that if you want to train something to predict physics, the original physics simulation that's generating the data is either:

1. Already doing that, in which case it's already the superior model.

or

2. Not doing that correctly, in which case the synthetic data output is equally poor.

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Consider computer graphics and computer vision. If you had a photorealistic renderer, it could be useful for generating training data for a vision algorithm. However, you can't just run your rendering pipeline backwards to create a scene out of an image.

I mean, if (1) is true but you get faster performance, then it's useful, right?

Yes. A typical example would be the use of statistical emulators, emulating process (physical) based models that are computationally complex in the environmental sciences.

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