openAI-math-dataset / lean /OAI /Geometry /IsometricImmersion /Darboux /SixVariableLinearization.lean
roseking's picture
Add lean source and binary files (part 69)
59d1c80 verified
Raw History Blame Contribute Delete
8.03 kB
import OAI.Geometry.IsometricImmersion.Calculus.VerticalSliceJets
import OAI.Geometry.IsometricImmersion.Darboux.ActualDriftEquation
import Mathlib.Analysis.Calculus.Deriv.Pi
namespace OAI
noncomputable section
open Set Function
open scoped ContDiff Matrix
namespace SmoothLocal.HighEquation
open SmoothLocal.Geometry
theorem fderiv_axis_eq_deriv_update {n : ℕ} {f : (Fin n → ℝ) → ℝ} {w : Fin n → ℝ}
(hf : DifferentiableAt ℝ f w) (i : Fin n) :
fderiv ℝ f w (Pi.single i 1) = deriv (fun t => f (Function.update w i t)) (w i) := by
have hd : HasFDerivAt f (fderiv ℝ f w) (Function.update w i (w i)) := by
simpa using hf.hasFDerivAt
exact (hd.comp_hasDerivAt (w i) (hasDerivAt_update w i (w i))).deriv.symm
def gradientStateIndex (i : Fin 2) : Fin 6 := if i = 0 then 2 else 3
theorem sixVariableP_update_gradient (g : MetricField) (w : DarbouxState) (i : Fin 2) (t : ℝ) :
sixVariableP g (Function.update w (gradientStateIndex i) t) =
solvedDarboux g (statePoint w) (w 4) (w 5) (Function.update (stateGradient w) i t) := by
have hp : statePoint (Function.update w (gradientStateIndex i) t) = statePoint w := by
ext j
fin_cases i <;> fin_cases j <;> simp [gradientStateIndex, statePoint]
have hv : stateGradient (Function.update w (gradientStateIndex i) t) =
Function.update (stateGradient w) i t := by
ext j
fin_cases i <;> fin_cases j <;> simp [gradientStateIndex, stateGradient]
unfold sixVariableP
rw [hp, hv]
fin_cases i <;> simp [gradientStateIndex]
theorem sixVariableP_update_mixed (g : MetricField) (w : DarbouxState) (t : ℝ) :
sixVariableP g (Function.update w 4 t) =
solvedDarboux g (statePoint w) t (w 5) (stateGradient w) := by
have hp : statePoint (Function.update w 4 t) = statePoint w := by
ext j
fin_cases j <;> simp [statePoint]
have hv : stateGradient (Function.update w 4 t) = stateGradient w := by
ext j
fin_cases j <;> simp [stateGradient]
unfold sixVariableP
rw [hp, hv]
simp
theorem sixVariableP_update_yy (g : MetricField) (w : DarbouxState) (t : ℝ) :
sixVariableP g (Function.update w 5 t) =
solvedDarboux g (statePoint w) (w 4) t (stateGradient w) := by
have hp : statePoint (Function.update w 5 t) = statePoint w := by
ext j
fin_cases j <;> simp [statePoint]
have hv : stateGradient (Function.update w 5 t) = stateGradient w := by
ext j
fin_cases j <;> simp [stateGradient]
unfold sixVariableP
rw [hp, hv]
simp
theorem solvedDarboux_first_variables_differentiableAt (g : MetricField) (p u : Coord) (b c : ℝ)
(hyy : jetYY g p c u ≠ 0) : DifferentiableAt ℝ (solvedDarboux g p b c) u := by
have hd := (jetConnection_differentiableAt g p u 0 0).add
((jetNumerator_differentiableAt g p u b).mul ((jetYY_differentiableAt g p u c).inv hyy))
exact hd
theorem sixVariableP_gradient_axis
{g : MetricField} {U : Set Coord} {w : DarbouxState}
(hg : SmoothPositiveOn g U) (hU : IsOpen U) (hw : w ∈ darbouxStateDomain g U) (i : Fin 2) :
fderiv ℝ (sixVariableP g) w (Pi.single (gradientStateIndex i) 1) =
coordPartial i (solvedDarboux g (statePoint w) (w 4) (w 5)) (stateGradient w) := by
have hP : DifferentiableAt ℝ (sixVariableP g) w :=
((sixVariableP_contDiffOn hg hU).contDiffAt
((darbouxStateDomain_isOpen hg hU).mem_nhds hw)).differentiableAt (by simp)
rw [fderiv_axis_eq_deriv_update hP]
have heq : (fun t => sixVariableP g (Function.update w (gradientStateIndex i) t)) =
(fun t => solvedDarboux g (statePoint w) (w 4) (w 5) (Function.update (stateGradient w) i t)) :=
funext (sixVariableP_update_gradient g w i)
rw [heq]
have hi : w (gradientStateIndex i) = stateGradient w i := by
fin_cases i <;> simp [gradientStateIndex, stateGradient]
rw [hi]
exact (fderiv_axis_eq_deriv_update
(solvedDarboux_first_variables_differentiableAt g (statePoint w) (stateGradient w) (w 4) (w 5) hw.2) i).symm
theorem sixVariableP_gradient_axis_at_height
{g : MetricField} {z : Coord → ℝ} {U : Set Coord} {p : Coord}
(hg : SmoothPositiveOn g U) (hU : IsOpen U) (hp : p ∈ U)
(hyy : covHessian g z p 1 1 ≠ 0) (i : Fin 2) :
fderiv ℝ (sixVariableP g) (solutionJet z p) (Pi.single (gradientStateIndex i) 1) =
heightPFirst g z i p := by
rw [sixVariableP_gradient_axis hg hU (solutionJet_mem_domain g z hp hyy) i,
statePoint_solutionJet, stateGradient_solutionJet]
rfl
theorem sixVariableP_mixed_axis_at_height
{g : MetricField} {z : Coord → ℝ} {U : Set Coord} {p : Coord}
(hg : SmoothPositiveOn g U) (hU : IsOpen U) (hp : p ∈ U)
(hyy : covHessian g z p 1 1 ≠ 0) :
fderiv ℝ (sixVariableP g) (solutionJet z p) (Pi.single 4 1) = 2 * hessianQuotient g z p := by
have hP := (sixVariableP_contDiffAt_solutionJet hg hU hp hyy).differentiableAt (by simp)
rw [fderiv_axis_eq_deriv_update hP]
have heq : (fun t => sixVariableP g (Function.update (solutionJet z p) 4 t)) =
(fun t => solvedDarboux g p t (coordPartial 1 (coordPartial 1 z) p) (fun i => coordPartial i z p)) := by
funext t
rw [sixVariableP_update_mixed, statePoint_solutionJet, stateGradient_solutionJet]
rfl
rw [heq]
change deriv (fun t => solvedDarboux g p t (coordPartial 1 (coordPartial 1 z) p)
(fun i => coordPartial i z p)) (coordPartial 0 (coordPartial 1 z) p) = _
rw [solvedDarboux_deriv_b g p _ _ _ hyy, jetMixed_at_height, jetYY_at_height]
rfl
theorem sixVariableP_yy_axis_at_height
{g : MetricField} {z : Coord → ℝ} {U : Set Coord} {p : Coord}
(hg : SmoothPositiveOn g U) (hU : IsOpen U) (hp : p ∈ U)
(hyy : covHessian g z p 1 1 ≠ 0) :
fderiv ℝ (sixVariableP g) (solutionJet z p) (Pi.single 5 1) =
-((hessianQuotient g z p)^2 + gaussianCurvature g p * darbouxG g z p) := by
have hP := (sixVariableP_contDiffAt_solutionJet hg hU hp hyy).differentiableAt (by simp)
rw [fderiv_axis_eq_deriv_update hP]
have heq : (fun t => sixVariableP g (Function.update (solutionJet z p) 5 t)) =
(fun t => solvedDarboux g p (coordPartial 0 (coordPartial 1 z) p) t (fun i => coordPartial i z p)) := by
funext t
rw [sixVariableP_update_yy, statePoint_solutionJet, stateGradient_solutionJet]
rfl
rw [heq]
change deriv (fun t => solvedDarboux g p (coordPartial 0 (coordPartial 1 z) p) t
(fun i => coordPartial i z p)) (coordPartial 1 (coordPartial 1 z) p) = _
rw [solvedDarboux_deriv_c g p _ _ _ hyy, jetMixed_at_height, jetYY_at_height,
jetEnergy_at_height hg hp z]
unfold hessianQuotient darbouxG
ring
theorem sixVariableP_vertical_direction
{g : MetricField} {z : Coord → ℝ} {U : Set Coord} {p : Coord}
(hg : SmoothPositiveOn g U) (hU : IsOpen U) (hp : p ∈ U)
(hyy : covHessian g z p 1 1 ≠ 0) (v : DarbouxState) (hv0 : v 0 = 0) (hv1 : v 1 = 0) :
fderiv ℝ (sixVariableP g) (solutionJet z p) v =
heightPFirst g z 0 p * v 2 + heightPFirst g z 1 p * v 3 +
2 * hessianQuotient g z p * v 4 -
((hessianQuotient g z p)^2 + gaussianCurvature g p * darbouxG g z p) * v 5 := by
have he : v = v 2 • (Pi.single 2 (1 : ℝ) : DarbouxState) +
v 3 • Pi.single 3 1 + v 4 • Pi.single 4 1 + v 5 • Pi.single 5 1 := by
ext i
fin_cases i <;> simp [hv0, hv1]
have h0 : fderiv ℝ (sixVariableP g) (solutionJet z p) (Pi.single 2 1) = heightPFirst g z 0 p := by
simpa only [gradientStateIndex, ↓reduceIte] using sixVariableP_gradient_axis_at_height hg hU hp hyy 0
have h1 : fderiv ℝ (sixVariableP g) (solutionJet z p) (Pi.single 3 1) = heightPFirst g z 1 p := by
simpa only [gradientStateIndex, show (1 : Fin 2) ≠ 0 by decide, ↓reduceIte] using
sixVariableP_gradient_axis_at_height hg hU hp hyy 1
have hl := congrArg (fderiv ℝ (sixVariableP g) (solutionJet z p)) he
simp only [map_add, map_smul, h0, h1, sixVariableP_mixed_axis_at_height hg hU hp hyy,
sixVariableP_yy_axis_at_height hg hU hp hyy, smul_eq_mul] at hl
rw [hl]
ring
end SmoothLocal.HighEquation
end
end OAI