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Project-declaredLean 4.32.0 · mathlib@81a5d257c8e4

Euler lagrange var Gradient

ClassicalMechanics.euler_lagrange_varGradient

Project documentation

The Euler Lagrange operator, for a trajectory q : Time → X, and a lagrangian Time → X → X → ℝ, the Euler-Lagrange operator is ∂L/∂q - dₜ(∂L/∂(dₜ q)). -/ noncomputable def eulerLagrangeOp (L : Time → X → X → ℝ) (q : Time → X) : Time → X := fun t => gradient (L t · (∂ₜ q t)) (q t) - ∂ₜ (fun t' => gradient (L t' (q t') ·) (∂ₜ q t')) t lemma eulerLagran...

Exact Lean statement

theorem euler_lagrange_varGradient
    (L : Time → X → X → ℝ) (q : Time → X)
    (hq : ContDiff ℝ ∞ q) (hL : ContDiff ℝ ∞ ↿L) :
    (δ (q':=q), ∫ t, L t (q' t) (fderiv ℝ q' t 1)) = eulerLagrangeOp L q

Formal artifact

Lean source

Canonical source
Full Lean sourceLean 4
theorem euler_lagrange_varGradient    (L : Time  X  X  ) (q : Time  X)    (hq : ContDiff  ∞ q) (hL : ContDiff  ∞ ↿L) :    (δ (q':=q), ∫ t, L t (q' t) (fderiv  q' t 1)) = eulerLagrangeOp L q := by  simp only [eulerLagrangeOp_eq, Time.deriv_eq]  apply HasVarGradientAt.varGradient  apply HasVarGradientAt.intro _  · apply HasVarAdjDerivAt.comp      (F := fun (φ : Time  X × X) t => L t (φ t).fst (φ t).snd)      (G := fun (φ : Time  X) t => (φ t, fderiv  φ t 1))    · apply HasVarAdjDerivAt.fmap (f := fun t => ↿(L t))      · fun_prop      · fun_prop      intro x u      apply DifferentiableAt.hasAdjFDerivAt      apply ContDiff.differentiable (n := ∞) (by fun_prop) (by simp)    · apply HasVarAdjDerivAt.prod (F:=fun φ => φ)      · apply HasVarAdjDerivAt.id _ hq      · apply HasVarAdjDerivAt.fderiv (hu := hq)  case hgrad =>    funext t    simp (disch := fun_prop) [sub_eq_add_neg]    congr    all_goals      try funext t      rw [gradient_eq_adjFDeriv, adjFDeriv_uncurry] <;>        apply ContDiff.differentiable (n := ∞) (by fun_prop) (by simp)
Project
Physlib
License
Apache-2.0
Commit
dd43e9e65791
Source
Physlib/ClassicalMechanics/EulerLagrange.lean:44-70

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