Metric Modeling and Representation of 4D Data through 3D Vector Tensoring
In representational, metric analytics, it is often made use of binary vectors, classically drafted in two-dimensional (2D) grids. However, these do not reflect complexity in manifest, part-observable reality.
Tensors be 4D-computed in order to be 3D-geometrically represented. This can be done by applying data to a three-dimensional grid containing dynamic, non-binary-graph and 3D vector objects changing, in different scenarios, on the time axes, generating multiple grids. Scenarios can be limited by applying probability weighing or targeting specific results, by eliminating transitions and breaks, or else intermittent vector changes.
If one attempted to simplify the Tensor representation neglecting the shift representations in time, one would replace many 3D, non-binary-graph vector objects with fewer complex, but 4D-grafically representable objects in time, resulting in vector folding. This is of benefit in posterio, for typical aggregates. In practical applications, and in comparative settings, one will rather apply unfolded 3D objects in stage representations, adhering to 3D Vector Tensoring, or Plurigrafity.