This _is_ computational filtering. It's not philosophically any different. Every filtering method algorithmically guesses what's important or what's real and what's not.

> It's not philosophically any different.

I disagree. I think using techniques that work by attempting to model physical processes that we understand are philosophically different from ML approaches that are learning arbitrary functions.

I agree there is a gulf of difference between modelling based on physics and mere empirical modelling fitting functions to data

I agree with your earlier point, but disputing the usage of the term computational filtering here is truly pedantic. Yes, by definition machine learning approaches are a subset of computational approaches, but there are clear differences in terms of (at least) failure modes between machine learning and other techniques. In context, "non-machine-learning based filtering methods" is what was being referred to.

Importantly, the internals of non-machine-learning based approaches are more readily understandable and their output is much more predictable.