Documentation

LeanPool.Superdiffusion.Algsuperdiff.Section3.Provider.Diffusivity.ApproximateRecurrence.Closure.GammaTenAssemblySide

GammaTenAssemblySide #

Ported from scottnarmstrong/Superdiffusion at 7f20476385d4af418f110303085ad0740c9eba46. Module imports, support namespace and local option directives adapted for Lean Pool.

The analytic side conditions of the per-cube fluctuation bound #

ABK26, Step 2 of l.approximate.recurrence.formula, at the carrier of e.lower.bound.localization.terms.

The gap this module fills #

LocalizationFluctuationPerCube.perCube_localizationFluctuation_le is stated with eight analytic side conditions --- the six L^2 memberships hwDmem, hwNmem, hY1, hY2, h, h at the normalized cube measure, and the two spatial integrabilities hInt1, hInt2 of the gauged pairing integrands --- which its own docstring records as "conditional A obligations of this module, discharged by the caller at its carrier". This module discharges all eight, once and for all, at the carriers a consumer of Step 2 reads them at.

Nothing here is a source display: every statement below is a transport between two spellings of L^2 on one triadic cube, an application of the block ellipticity bound of Section24.Sensitivity.Provider.Path.Densities, or a Cauchy--Schwarz.

What is proved #

The eight are consumed together, in the order and shape perCube_localizationFluctuation_le reads them, by Closure.GammaTenAssemblyCell.

Binders #

The geometry hsub : openCubeSet R ⊆ openCubeSet Q (which is what makes the correctors of the localization cube Q readable on the cell R) and, for the two hY statements and the two integrabilities, the two L^2 legs of the response field. No estimate, no smallness, no moment and no measurability in the sample occurs: everything below is spatial, at one fixed sample point.

Scope #

There is no sorry, no admit, no custom axiom and no set_option maxHeartbeats.

References #

The two carrier transports #

The transport of item 1: vector L^2 on the cell becomes L^2 of the normalized cube measure.

This is the converse of CoarseGraining's memVectorL2_cubeSet_of_memLp_normalizedCubeMeasure.

The two L^2 legs of a response field #

hwDmem and hwNmem #

hwNmem. The flux leg field grad w_N + shom^{-1} h e' of e.Pz.def is L^2 of the normalized measure of every cell: the gradient by H1Function.grad_memVectorL2, the stream forcing because it is continuous.

h and h #

hY1 and hY2: the CoeffOn.Lam-boundedness step #

hY1. The potential component of bfA(a) T is L^2 of the normalized cube measure whenever both legs of T are vector L^2.

The content is the uniform ellipticity of a (lam <= . <= Lam), which bounds the four blocks of blockMatrixField a a.e.; that step is CoarseGraining-side, proved as Section24.Sensitivity.Provider.Path.memVectorL2_blockMatrixField_fst.

hInt1 and hInt2: Cauchy--Schwarz of two L^2 fields #