We feel that the potential value of this work lies in combining two established computational workflows: Questionnaires (which organizes data) and neural operator learning (which learns evolution equations capable of reproducing the data-and thus generating the equations). It is the combination that enables the construction of meaningful, compact, generative models from previously unusable data, thus broadening the scope of both individual workflows. In our opinion, the most promising avenue for our computational framework would be for the use of studying multiscale problems, where different data organizations and different dynamical descriptions coexist at different scales. An obvious and significant application would be biomedical problems, where descriptions at, say, the cellular, the tissue, the organ, and the organism level are concurrently valid. A possible dream would be to extend the mouse brain activity data organization originally attempted in to dynamic behavioral models.