Our goal is to present a comprehensive, general framework that combines unsupervised learning with state-of-the-art generative NN-based modeling techniques, enhancing our ability to construct accurate and robust models from disorganized and potentially unlabelled datasets. Our approach uniquely integrates an unsupervised learning method of choice (Questionnaires ) with state-of-the-art ML approaches such as deep hidden physics models (DHPM) and deep neural operators (NOs) , offering an effective way to incorporate parameters into the model and create a fully data-driven system identification framework. In this context, we build emergent NOs assisted by the questionnaire metric. We demonstrate our framework (visually illustrated in Figure ) first on the previously discussed underground winding pipeline example (modeled as a 1D advection-diffusion equation and artificially scrambled), and then on an ensemble of Stuart-Landau oscillators , where a coherent spatial dimension is inherently hidden. The contributions of this work can be summarized as follows: