We have looked at LDA/QDA several times during my stats masters coursework, but I'm not convinced that it's due to the usefulness of the techniques more than my school being stuck with a 20-year old curriculum.
This answer by Frank Harrell suggests that these techniques aren't very useful nowadays, and even the textbook we're using indicates that LDA/QDA is only expected to perform significantly better than logistic regression when the assumptions are met, which seems fairly disqualifying for most purposes.
To make my title request more specific, I'm looking for at least two different examples (i.e. not the same type of problem, preferably different disciplines) that are
Good: clearly the most optimal tool to solve the problem at hand, not just being used in place of logistic regression because of researcher preference
Recent: published in the last six years
"... or else": need not be textbook LDA/QDA. It's okay if the technique is an extension of the above models, but it should obviously follow the same reasoning i.e. related to a decision rule based on distributional assumptions on predictors conditional to the outcome of interest
Alternatively, it would also be acceptable if someone can provide proof that any formulation of a discriminant-type model can be re-expressed as a regression problem (e.g. linear regression produces results equivalent to LDA).