I'm not in the field (nor an academic, nor particularly smart), but my impression is that this is implicit in nearly everything people are doing. Almost all of

I'm not in the field (nor an academic, nor particularly smart), but my impression is that this is implicit in nearly everything people are doing. Almost all of the papers will be talking about interpolation, not extrapolation. More specifically, training data and test data are assumed to be partitioning an existing data set into test and training portions. "Generalization" is measured only by success at fitting the test data. Of course, most actual applications immediately break out of that model by running live against previously unobserved data coming from different populations, different times (just think: post-2020 vs pre-2020!), often different purposes. And probably much of the error because you're now extrapolating gets regarded as an engineering problem?