In a realistic scenario, it is not uncommon to lose track of the true z labels and experiment identifiers. Historical data may lack recorded parameters such as diffusion coefficients (D), velocity (v), or even fundamental physics knowledge, with only the origin of the data from various experiments known. Even if we do not know the exact spatial location of a given sensor label, the spatial location of the sensors must remain the same throughout all experiments (for example, we do not pick up sensor A and move it to a new location on the pipe). The sensor labels along the pipe are often disordered spatially; for instance, the pipe may wind underground while measurements are confined to a uniformly spaced Euclidean grid. Alternatively, different teams might measure distinct sections of the pipe, using team names or sensor labels (e.g., sample point 312C) as spatial markers. To illustrate our approach with a 3D tensor, we also forego a perfect temporal ordering. Consequently, while the experiment, spatial, and temporal sensors (channels) remain consistent across experiments, the data appear scrambled and shuffled, as depicted in Figure . Our task is to salvage this scrambled data and build a useful deep-learning model for predicting tracer concentration.