I have two white papers on transporting shape types to difference equations on images; and am working on a third related to models of Zn.
https://www.zmgsabstract.com/whitepapers/shapes-as-digital-i...
https://www.zmgsabstract.com/whitepapers/shapes-have-operati...
This is pretty new; but the main interest is actually the inverse mapping — can you recover a type theory that is the “internal language” of the diagram modeling the difference equations that (eg) represents a DNN?
That would be the “effective semantics” of the DNN, expressed as typed statements.
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I’m following the work of (eg) Michael Shulman on attaching types to categories; and my own experience as an SDE on the logic, types, difference equation side. (There’s some papers on programs as Euclidean VMs I used as well, but I don’t have them handy.)
The weakest link is category to difference equations; but that exists to some degree already due to physics — and I’m muddling my way through implementing that to gain deeper insight myself.