Existence of the reconstruction of a modelled distribution for
OpenHairer.reconstruction_existence_pointwise_nonposExistence half of Hairer's Theorem 3.10 in the range , 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 with
the second bound holding, for every compact set with a constant , uniformly over , and .
For the bound no longer determines , which is why Theorem 3.10 also asserts that a linear choice exists. That linearity is not part of this statement: once each modelled distribution is reconstructed individually, the pairs satisfying the two displayed conditions form a linear subspace that surjects onto , and a linear section of that surjection turns the pointwise statement into a linear operator. What remains here is therefore the analytic content of §3.1 in the non-positive range: the multiscale construction of a distribution matching the local jets to order .
The complementary range is excluded because it is degenerate: there every modelled distribution vanishes identically.
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
`α < γ ≤ 0`, stated for a single modelled distribution.
For `f ∈ D^γ` 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` this bound no longer determines `ξ`; no linearity in `f` is
asserted here, a linear choice being obtainable from the pointwise statement.
The range `γ ≤ α` is excluded because it is degenerate: there the only modelled
distribution is `f = 0`. -/
theorem reconstruction_existence_pointwise_nonpos
{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} (hγα : α < γ) (hγ : γ ≤ 0)
{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