Documentation

LeanPool.AnomalousDiffusion.AVenhance.Infra.FaaDiBruno.TransportCharacteristicJets

Transport Characteristic Jets #

Support for the Armstrong–Vicol anomalous-diffusion formalization.

theorem AVenhance.FaaDiBruno.list_ofFn_map {α : Type u_1} {β : Type u_2} {n : ℕ} (I : Fin n → α) (f : α → β) :
(List.ofFn fun (j : Fin n) => f (I j)) = List.map f (List.ofFn I)

A list of coordinate directions is one of the ordered partials used in the paper's seminorm.

theorem AVenhance.FaaDiBruno.TransportCharacteristicJets.list_sum_norm_le_of_forall {α : Type u_1} (L : List α) (f : α → Homogenization.Vec 2) (g : α → ℝ) (hfg : ∀ a ∈ L, ‖f a‖ ≤ g a) :
theorem AVenhance.FaaDiBruno.TransportCharacteristicJets.list_sum_add_const {α : Type u_1} (L : List α) (g : α → ℝ) (c : ℝ) :
(List.map (fun (a : α) => c + g a) L).sum = ↑L.length * c + (List.map g L).sum
theorem AVenhance.FaaDiBruno.TransportCharacteristicJets.list_sum_le_of_forall {α : Type u_1} (L : List α) (f g : α → ℝ) (hfg : ∀ a ∈ L, f a ≤ g a) :
theorem AVenhance.FaaDiBruno.TransportCharacteristicJets.list_sum_map_add {α : Type u_1} (L : List α) (f g : α → ℝ) :
(List.map (fun (a : α) => f a + g a) L).sum = (List.map f L).sum + (List.map g L).sum
theorem AVenhance.FaaDiBruno.TransportCharacteristicJets.list_sum_if_count {α : Type u_1} (L : List α) (p : α → Prop) [DecidablePred p] (c : ℝ) :
(List.map (fun (a : α) => if p a then c else 0) L).sum = ↑(List.countP (fun (a : α) => decide (p a)) L) * c
theorem AVenhance.FaaDiBruno.transportSourceJet_coordinate_norm_le_local {n : ℕ} (I : Fin n → Fin 2) {b g Y : ℝ → Homogenization.Vec 2 → Homogenization.Vec 2} (hb : ContDiff ℝ (↑⊤) (Function.uncurry b)) (hg : ContDiff ℝ (↑⊤) (Function.uncurry g)) (hY : ContDiff ℝ (↑⊤) (Function.uncurry Y)) {C_f C_g R U T : ℝ} (RY : ℝ → ℝ) (hR : 0 < R) (hRY : ∀ (t : ℝ), 0 < RY t) (hCf : 0 ≤ C_f) (hCg : 0 ≤ C_g) (A : ℝ → ℕ → ℝ) (hA : ∀ (t : ℝ) (k : ℕ), 0 ≤ A t k) (hB : ∀ (t : ℝ) (m : ℕ), 1 ≤ m → m ≤ n → snorm (b t) m R ≤ ENNReal.ofReal C_f) (hG : ∀ (t : ℝ), snorm (g t) n R ≤ ENNReal.ofReal C_g) (hYbound : ∀ (t : ℝ), |t| ≤ T → ∀ (k : ℕ), 1 ≤ k → k < n → snorm (Y t) k (RY t) ≤ ENNReal.ofReal (A t k)) (t : ℝ) (ht : |t| ≤ T) (x : Homogenization.Vec 2) (hUjet : ∀ (J : Fin n → Fin 2), ‖orderedPartial n (Y t) x J‖ ≤ U) :
‖transportSourceJet (List.ofFn fun (j : Fin n) => coordinateVector 2 (I j)) b g Y (t, x)‖ ≤ C_g * ↑n.factorial * R ^ n / (↑n + 1) ^ 2 + 2 * (↑n * (C_f * R / 4 * U) + (List.map (fun (p : List (Fin 2) × List (Fin 2)) => if 2 ≤ p.1.length then C_f * ↑p.1.length.factorial * R ^ p.1.length / (↑p.1.length + 1) ^ 2 * (A t (p.2.length + 1) * ↑(p.2.length + 1).factorial * RY t ^ (p.2.length + 1) / (↑p.2.length + 2) ^ 2) else 0) (directionalSplitsNonempty (List.ofFn I))).sum)

A pointwise source estimate with the current order-n jet left as a linear term. This is the a-priori finiteness estimate used before the sharp corrected absorption.

The joint spatial jet indexed by a coordinate tuple is the paper's ordered coordinate partial of the time slice.

Every nonempty spatial jet of a zero-initial-data transport solution is the integral of its recursively differentiated source along a characteristic.