# Partial Differential Equations/Fundamental solutions, Green's functions and Green's kernels

In the last two chapters, we have studied test function spaces and distributions. In this chapter we will demonstrate a method to obtain solutions to linear partial differential equations which uses test function spaces and distributions.

## Fundamental Solutions[edit]

In the last chapter, we had defined multiplication of a distribution with a smooth function and derivatives of distributions. Therefore, for a distribution , we are able to calculate such expressions as

for a smooth function and a -dimensional multiindex . We therefore observe that in a linear partial differential equation of the form

we could insert any distribution instead of in the left hand side. However, equality would not hold in this case, because on the right hand side we have a function, but the left hand side would give us a distribution (as finite sums of distributions are distributions again due to theorem 4.?; remember that only finitely many are allowed to be nonzero). If we however replace the right hand side by (the regular distribution corresponding to ), then there might be distributions which satisfy the equation. In this case, we speak of a *distributional solution*. Let's summarise this definition in a box.

**Definition 5.?**:

Let be open, let

be a linear partial differential equation, and let . is called a **distributional solution** to the above linear partial differential equation iff

Now we will show how we can obtain distributional solutions to a partial differential equation. The method of choice will be to guess a so-called *fundamental solution* and then construct solutions with the help of that fundamental solution.

**Definition 5.?**:

Let be open and let

be a linear homogenous partial differential equation. If has the two properties

, we call a **fundamental solution**.

Now why we defined this is: Once we have a fundamental solution for the homogenous equation (i. e. ), we can easily construct solutions to the inhomogenous problem. We shall now explain how this works.

**Lemma 5.?**:

Let be a family of distributions, where . Let's further assume that for all , the function is continuous on and bounded, and let . Then

is a distribution.

*Proof*: Due to the truncation of -functions, we have that there are radii such that

, where is the supremum of the function .

is a compact set, since it is bounded as well as closed. Therefore, we may divide into finitely many (let's say ) squares with diameter at most , such that

. This we may do because continuous functions are uniformly continuous on compact sets. At the border, we just round the squares so that they fit in with the sphere. Furthermore, we choose for each square a inside this square.

We choose now

, which is a finite linear combination of distributions and therefore a distribution. Due to the normal triangle inequality for the absolute value, the triangle inequality for the Lebegue integral, our first calculation and the fundamental integral estimation, we obtain:

This obviously goes to zero, and this lemma follows with Lemma 2.1.

Let's assume that in equation , is integrable. Let be a fundamental solution for with respect to the locally convex normed function space , such that , the function is bounded. Then we can know, that:

is well-defined and solves in the sense of distributions.

*Proof*: Since by the definition of fundamental solutions, the function is continuous, we may apply lemma 2.2, which gives us that is indeed well-defined.

To show that it really solves in the sense of distributions, we need the following calculation:

, which is what we wanted to show.

## Green's functions[edit]

Assume that for each , the fundamental solution is a regular distribution, i. e. for each , there is an integrable function such that . Then we call this function a *Green's function* for .

## Green's kernels[edit]

Let's assume that has the Green's function . If there exists a function such that

, then we call a *Green's kernel* for .

Let be a locally integrable function, and be a domain. Then the family of distributions is well-defined and depends continuously on . Furthermore, for each , the function is bounded.

*Proof*: Well-definedness follows from Lemma 1.3.

Let , and let . Then we can calculate the following:

for sufficiently large , where the last expression goes to as , since the support of is compact and therefore the function is (even uniformly) continuous.

Furthermore, we have

, which is zero for sufficiently large, which is why the function has compact support. But since the function is also continuous, we know that it obtains a maximum and a minimum and is therefore bounded.

This lemma shows that if we have found a locally integrable function such that , we already know that it is a Green's kernel, and don't need to check the continuity property.

**Theorem 5.?**: (Fubini's theorem)

Let and , where are arbitrary natural numbers, and let be a function. Then

Now this theorem finally shows us why distributions are useful:

Let be a Green's kernel for , and let . If

is sufficiently often differentiable such that is continuous, then it is a solution for in the classical sense.

*Proof*: From a case of Hölder's inequality (namely , i. e. ), we obtain that is locally integrable, which is why is a distribution in .

Furthermore, due to the theorem of Fubini, we have for , that

, which is why solves in the sense of distributions (this is due to theorem 2.3).

Thus, for all , we can calculate the following:

and therefore

- .

From this follows that almost everywhere. But since and are both continuous, they must be equal everywhere. This is what we wanted to prove.