This goes far beyond recent applications of neural networks in astrophysics, which were limited to solving classification problems, such as determining whether an image shows a gravitational lens or not.'
Pretty fascinating stuff. Once you think about it, applying NNs to space makes a lot of sense. There is a ton of data to sift through and find patterns in. Amazing to think neural nets could crunch through this data in seconds, and point out areas of interest immediately. I wonder if NNs have been used in the search for exo planets yet.
Might be kind of overkill. The patterns being looked at for exoplanets are periodical dimming of stars AFAIK. I don't think you necessarily need a neural network to sift through that.
To train the neural networks in what to look for, the researchers showed them about half a million simulated images of gravitational lenses for about a day.
I'm not sure what you mean by a "real models" in any case.
Things like NN based generative models combined with model selection certainly can build models which discover real-world behavior. There's a long history of this in the disease epidemiology field. In these cases it isn't usually neural network based, but that is mostly about the most appropriate learning algorithm for the data available.
A NN can sureley result in a traditional causal model.
For example: Build a NN that has the same computational structure as some simple physics law. By giving it training data it then figures out necessary constants. That may converge to the traditional model, which is mirrowed in the network architecture anyway.
So I really can't support saying NN will by their nature NEVER be causal.
I'm not sure what you think explicit construction proves, it is clearly a case of taking preexisting knowledge and expressing it in what is doubthlessly a more akward form.