machine learning inside a physical model

A my loyal reader knows, I love putting machine learning inside a physical model. That is, not just using machine learning, but re-purposing machine learning to play a role in modeling a nuisance we don't care about inside our physical model. It's similar to how the best methods for causal inference use machine learning to capture the possibly complex and adversarial effects of confounders. Today I had the pleasure of reading closely a new manuscript by Francois Lanusse (Paris) that describes a use of machine learning to model galaxy images, but putting that model inside a causal structure (think: directed acyclic graph) that includes telescope optics and photon noise. The method seems to work really well.