[waterheater]

Without changing the fundamental learning process, one could conceivably introduce a "post-production" step, where you tighten up the output according to a set of pre-defined rules (e.g., if an angle is 89 degrees, adjust the angle to 90).

Of course, changing the learning process would be best. One idea which comes to mind is finding a way to embed relationships into the ML training system itself (e.g., output no angles other than 90 degrees or some predefined set). Such an approach is a type of contraint-based ML, where the ML agent identifies a solution given certain constraints on the output. In my experience, the right approach to accomplish this goal is using factor graphs.