I think it’s a very poor analogy.
First, a glance at the original paper reveals that the neural network has a convolutional architecture. I.e., it’s a CNN. A CNN uses “representation learning” to discover, without human intervention, the features in the input data that are useful for making accurate predictions. These features typically involve some sort of transformation (filtering, aggregation, projection, etc.) to represent the input. And since it is a machine that has learned the representations, we humans often find the representations difficult to grasp. Here’s an example, of features that a CNN might use to detect an automobile:
In the course of performing its representation learning, the “Deep Density Displacement Model” seems to have discovered a mathematical representation of dark matter that is useful for predicting the evolution of large-scale structures. That’s why it is able to answer other questions–about dark matter effects–that it was not explicitly trained for, in my opinion.
Is this ability surprising? Yes, in the sense that it could not have been deterministically predicted in advance of the experiment. But no, in the sense of the surprising things neural networks have been able to do. A neural network trained to make personalized movie recommendations might make pretty good book recommendations, for example, provided that the book purchase data could be provided in an input format similar to the format used for movie rentals.
Does that provide a little clarity? Feel free to push back or ask questions.