Existence of the reconstruction of a modelled distribution for
OpenHairer.reconstruction_existence_pointwise_posExistence half of Hairer's Theorem 3.10 for a positive exponent, stated for one modelled distribution.
Let be a regularity structure, let be a model for it on with scaling and test order , let , and let . Then for every modelled distribution there exists a distribution such that
the second bound holding, for every compact set with a constant , uniformly over , and all test functions .
Only the existence of for a single is asserted. For the displayed bound determines uniquely, so the linearity of the assignment — which is part of the statement of Theorem 3.10 — is a formal consequence of uniqueness and is deliberately not included here. This is the analytic core of the theorem in the positive range: Hairer obtains as the limit of a convergent multiscale approximation built from a wavelet multiresolution analysis adapted to the scaling (§3.1).
import Definitions.Def_Hairer_Model set_option autoImplicit false open scoped Classical DirectSum noncomputable section
namespace Hairer
/-- **Theorem 3.10 (existence of the reconstruction), Hairer 2014**, in the case `γ > 0`,
stated for a single modelled distribution.
For `f ∈ D^γ` with `γ > 0` there is a distribution `ξ ∈ C^α_s` such that, on every
compact set `K`, `|(ξ - Π_x f(x))(S^δ_{s,x} η)| ≲ δ^γ` uniformly over `x ∈ K`,
`δ ∈ (0,1]` and `η ∈ B^r_{s,0}`. For `γ > 0` such a `ξ` is unique, so the linearity of
`f ↦ ξ` is automatic and is not part of this statement. -/
theorem reconstruction_existence_pointwise_pos
{d : ℕ} {s : Fin d → ℕ} (hs : IsScaling s)
{A : Set ℝ} {E : A → Type} [∀ a : A, NormedAddCommGroup (E a)]
[∀ a : A, NormedSpace ℝ (E a)]
{G : Subgroup (ModelSpace A E ≃ₗ[ℝ] ModelSpace A E)} {one : ModelSpace A E}
(hT : IsRegularityStructure A E G one)
{r : ℕ} {Pi : Pt d → ModelSpace A E →ₗ[ℝ] Distrib d}
{Gam : Pt d → Pt d → ModelSpace A E ≃ₗ[ℝ] ModelSpace A E}
(hmod : IsModel s r G Pi Gam)
{α : ℝ} (hα : IsLeast A α) (hαneg : α < 0)
{γ : ℝ} (hγ : 0 < γ)
{f : Pt d → ModelSpace A E} (hf : IsModelled s γ Gam f) :
∃ ξ : Distrib d,
MemCalpha s α ξ ∧
∀ K : Set (Pt d), IsCompact K → ∃ C : ℝ, ∀ x ∈ K, ∀ δ : ℝ, 0 < δ → δ ≤ 1 →
∀ η : Pt d → ℝ, IsTestBall s r η →
|(ξ - Pi x (f x)).eval (scaledTest s δ x η)| ≤ C * δ ^ γ := by
sorry
end Hairer