It seems that there are two related but distinct ways to simplifying a model: 1. projections / disregarding some dimension, followed by some model in this late

It seems that there are two related but distinct ways to simplifying a model: 1. projections / disregarding some dimension, followed by some model in this latent space, followed by an injection back into the native space. The model is simpler by virtue of operating in a lower dimensional space. 2. using a "reduced-order model" or "surrogate model", but still operating in the same input/output space. The model is simpler by virtue of, e.g. using fewer derivatives or delays. And with 2, you can have have the simple model e.g. arise from first principles, and learn a _residual_ correction on top of it. And it sounds like the comment contains a bit of both. Am I getting that right?