We have developed a framework for constructing "structural" generative models, particularly for NOs, designed to handle differential equations using disorganized and potentially unlabelled datasets. This framework integrates unsupervised learning methods (such as Questionnaires) with advanced deep learning architectures, specifically DHPM and DeepONet. Our technique was first demonstrated on an advection-diffusion example using scrambled data. Subsequently, we applied it to an ensemble of Stuart-Landau oscillators, where no apparent physical space exists, but one emerges through our learning process. We believe this method holds significant potential across various fields, particularly in the chemical and pharmaceutical industries, where hidden spaces give rise to PDEs, or in biomedical data analysis, where patient records are often not clearly labelled and even intentionally obscured. It is crucial to emphasize that our approach does not and cannot map data back to its original form without additionally training an independent decoder (and luckily too, for privacy reasons in the biomedical case!). If we had access to the original data in an organized form, we would work within that real space rather than the emergent space. However, with sufficient labelling/landmarking of the original, unshuffled space, we could create a datadriven mapping between reality and emergent coordinates. This additional information could include calibration curves, partial sensor locations, or specific data point origins (landmarks). An important question arises: What if we apply our framework to a historical dataset without additional real-world information, and then obtain a new data point in real space that was not used in the original model construction? This is a plausible scenario: how can we integrate old, disorganized information with new, fully labelled information? This challenge extends beyond simply building an encoder, as we lack sufficient information to do so, and there may be multiple transformations between the new and historical data. We leave this question for future work.