PhysLean Documentation

PhysLean.SpaceAndTime.Space.Distributions

Distributions on space #

In this module we define the derivatives, gradient, divergence and curl of distributions on Space.

Contrary to the usual definition of derivatives on functions, when working with distributions one does not need to check that the function is differentiable to perform basic operations. This has the consequence that in a lot of cases, distributions are in fact somewhat easier to work with than functions.

Examples of distributions #

Distributions cover a wide range of objects that we use in physics.

The constant distribution on space #

noncomputable def Space.constD {M : Type} [NormedAddCommGroup M] [NormedSpace M] (d : ) (m : M) :

The constant distribution from Space d to a module M associated with m : M.

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    Derivatives #

    noncomputable def Space.derivD {M : Type} {d : } [NormedAddCommGroup M] [NormedSpace M] (μ : Fin d) :

    Given a distribution (function) f : Space d →d[ℝ] M the derivative of f in direction μ.

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      theorem Space.derivD_comm {M : Type} {d : } [NormedAddCommGroup M] [NormedSpace M] (μ ν : Fin d) (f : (Space d)→d[] M) :
      (derivD ν) ((derivD μ) f) = (derivD μ) ((derivD ν) f)
      @[simp]
      theorem Space.derivD_constD {M : Type} {d : } [NormedAddCommGroup M] [NormedSpace M] (μ : Fin d) (m : M) :
      (derivD μ) (constD d m) = 0

      The gradient #

      The gradient of a distribution (Space d) →d[ℝ] ℝ as a distribution (Space d) →d[ℝ] (EuclideanSpace ℝ (Fin d)).

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        theorem Space.gradD_inner_eq {d : } (f : (Space d)→d[] ) (η : SchwartzMap (Space d) ) (y : EuclideanSpace (Fin d)) :
        inner ((gradD f) η) y = (((Distribution.fderivD ) f) η) y
        @[simp]
        theorem Space.gradD_constD {d : } (m : ) :
        gradD (constD d m) = 0

        The divergence #

        The divergence of a distribution (Space d) →d[ℝ] (EuclideanSpace ℝ (Fin d)) as a distribution (Space d) →d[ℝ] ℝ.

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          theorem Space.divD_apply_eq_sum_fderivD {d : } (f : (Space d)→d[] EuclideanSpace (Fin d)) (η : SchwartzMap (Space d) ) :
          (divD f) η = i : Fin d, (((Distribution.fderivD ) f) η) (basis i) i
          @[simp]
          theorem Space.divD_constD {d : } (m : EuclideanSpace (Fin d)) :
          divD (constD d m) = 0

          The curl #

          The curl of a distribution Space →d[ℝ] (EuclideanSpace ℝ (Fin 3)) as a distribution Space →d[ℝ] (EuclideanSpace ℝ (Fin 3)).

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            @[simp]

            ## Vector identities

            @[simp]

            The curl of a grad is equal to zero.