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 dirac delta function.
- The potential 1/r (which is not defined at the origin).
- The Heaviside step function.
- Interfaces between materials, such as a charged sphere.
The constant distribution on space #
The constant distribution from Space d
to a module M
associated with
m : M
.
Equations
- Space.constD d m = Distribution.const ℝ (Space d) m
Instances For
Derivatives #
Given a distribution (function) f : Space d →d[ℝ] M
the derivative
of f
in direction μ
.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The gradient #
The gradient of a distribution (Space d) →d[ℝ] ℝ
as a distribution
(Space d) →d[ℝ] (EuclideanSpace ℝ (Fin d))
.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The divergence #
The divergence of a distribution (Space d) →d[ℝ] (EuclideanSpace ℝ (Fin d))
as a distribution
(Space d) →d[ℝ] ℝ
.
Equations
- One or more equations did not get rendered due to their size.
Instances For
The curl #
The curl of a distribution Space →d[ℝ] (EuclideanSpace ℝ (Fin 3))
as a distribution
Space →d[ℝ] (EuclideanSpace ℝ (Fin 3))
.
Equations
- One or more equations did not get rendered due to their size.
Instances For
## Vector identities