Today Kate Storey-Fisher (NYU) and I asked ourselves: How can we do machine learning on cosmological simulations? If we want to use permutation-invariant methods, we need to use something like graph structure, and you can't currently execute graph neural networks at the scale of millions or billions of particles. So we need permutation-invariant scalars and vectors produced from point clouds. We discussed a set of options in terms of taking spherical-harmonic transforms in shells, and then combining those into scalars and vectors. There's a lot of fun geometry involved! Not sure if we have a plan, yet.
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