Since spatial derivatives are present, though, they lead to small corrections and couple the geometries in different spatial points. One can visualize this by starting with an initial slice which is approximated by a collection of homogeneous patches. For some time, each patch evolves independently of the others, but this is not precisely true since coupling effects have been ignored. Moreover, each patch geometry evolves in a chaotic manner, which means that two initially nearby geometries depart rapidly from each other. The approximation can thus be maintained only if the patches are subdivided during the evolution, which goes on without limits in the approach to the singularity. There is, thus, more and more inhomogeneous structure being generated on arbitrarily small scales, which leads to a complicated picture of a general singularity.
This picture can be taken over to the effective behavior of the Bianchi IX model. Here, the patches do not evolve chaotically even though at larger volume they follow the classical behavior. The subdivision thus has to be done also for the initial effective evolution. At some point, however, when reflections on the potential walls stop, the evolution simplifies and subdivisions are no longer necessary. There is thus a lower bound to the scale of structure whose precise value depends on the initial geometries. Nevertheless, from the scale at which the potential walls break down one can show that structure formation stops at the latest when the discreteness scale of quantum geometry is reached . This can be seen as a consistency test of the theory since structure below the discreteness could not be supported by quantum geometry.
We have thus a glimpse on the inhomogeneous situation with a complicated but consistent approach to a general classical singularity. The methods involved, however, are not very robust since the BKL scenario, which even classically is still at the level of a conjecture for the general case [32, 168], would need to be available as an approximation to quantum geometry. For more reliable results the methods need to be refined to take into account inhomogeneities properly.
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