# Full discretisation of semi-linear stochastic wave equations driven by multiplicative noise

## Abstract

A fully discrete approximation of the semi-linear stochastic wave equation driven by multiplicative noise is presented. A standard linear finite element approximation is used in space and a stochastic trigonometric method for the temporal approximation. This explicit time integrator allows for mean-square error bounds independent of the space discretisation and thus do not suffer from a step size restriction as in the often used Störmer-Verlet-leap-frog scheme. Furthermore, it satisfies an almost trace formula (i. e., a linear drift of the expected value of the energy of the problem). Numerical experiments are presented and confirm the theoretical results.

## 1Introduction

We consider the numerical discretisation of semi-linear stochastic

wave equations of the form

where and , , is a bounded convex domain with polygonal boundary . The “” denotes the time derivative . Assumptions on the smoothness of the nonlinearities and will be given below. The stochastic process is an -valued (possibly cylindrical) -Wiener process with respect to a normal filtration on a filtered probability space . The initial data and are -measurable random variables. We will numerically solve this problem with a linear finite element method in space and a stochastic trigonometric method in time.

We refer to the introductions of [16] and [5] for the relevant literature on the spatial, respectively temporal, discretisation of stochastic (linear) wave equations. Further, the recent publication [22] presents a full discretisation of the wave equation with additive noise: a spectral Galerkin approximation is used in space and an adapted stochastic trigonometric method, using linear functionals of the noise as in [12], is employed in time. Furthermore, the time discretisation of nonlinear stochastic wave equations by stochastic trigonometric methods is analysed in [21]. Finally, let us mention the recent publication [6] which analyses convergence in of the stochastic trigonometric method applied to the one-dimensional nonlinear stochastic wave equation.

In the present publication, we prove mean-square convergence for the full discretisation to the exact solution to the nonlinear problem . Furthermore, using this result, we derive a geometric property of our numerical integrator, namely a trace formula. The trace formula (the linear drift of the expected value of the energy) for the exact solution of as well as for the finite element solution and the completely discrete solution are presented.

Strong approximations of stochastic wave equations are relevant in many real applications. For example, let us consider the motion of a strand of DNA floating in a liquid as presented in [9] and references therein. The motion of the DNA molecule may be modeled by a wave equation and the impact of the fluid’s molecules may be modeled by a stochastic force acting on the string. When two normally distant parts of the DNA get close enough, biological events, such as release of enzymes, occur. It is thus of interest to consider strong approximation of stochastic wave equations in such a situation.

The paper is organised as follows. We introduce some notations and mention some useful results in the next section. Section 3 presents a mean-square convergence analysis for our numerical discretisation. A trace formula for the exact and numerical solutions is given in Section 4. Finally, numerical experiments illustrating the rates of convergence and the trace formula of the numerical solution are given in the final section.

## 2Notations and useful results

Let and be separable Hilbert spaces with norms and respectively. We denote the space of bounded linear operators from to by , and we let be the set of Hilbert-Schmidt operators with norm

where is an arbitrary orthonormal basis of . If , then we write and . Let be a self-adjoint, positive semidefinite operator. We denote the space of Hilbert-Schmidt operators from to by with norm

For the stochastic wave equation , we define and denote the -norm by . Further, we set with .

Let be a filtered probability space and the space of -valued square integrable random variables with norm

Next, we define the space , for with norm

where are the eigenpairs of with orthonormal eigenvectors. We also introduce the space

with norm for and . Note that and . In the following we denote the scalar product by and recall the notation for the norm .

Denoting the velocity of the solution to our stochastic partial differential equation by , one can rewrite as

where , , , and . The operator with is the generator of a strongly continuous semigroup of bounded linear operators on , in fact, a unitary group.

Let be a quasi-uniform family of triangulations of the convex polygonal domain with and . Let be the space of piecewise linear continuous functions with respect to which are zero on the boundary of , and let denote the -orthogonal projector and the -orthogonal projector (Ritz projector). Thus,

The discrete Laplace operator is then defined by

We note that . We also define discrete variants of and by

and equipped with the norm . Finally, the finite element approximation of can then be written as

or in the abstract form

where , , and are as before, and with . Note the abuse of notation for the projection and similarly for . This will be used throughout the paper. Again, is the generator of a -semigroup on .

We study the equations and in their mild form

where the semigroups can be expressed as

with , , and .

In order to ensure existence and uniqueness of problem we shall assume that and , with for some regularity parameter , and that the functions and satisfy

for all in the first two inequalities and for all in the last one. Through the text, (or etc.) denotes a generic positive constant that may vary from line to line. We assume that the order of initial regularity so that the discrete initial value is well defined.

The proof of this lemma follows from [8], see also the proof of Theorem 2.1 in [21].

We now collect some results that we will use later on. Sketches of the proofs of these results are collected in the appendix at the end of this paper.

The error estimates for the cosine and sine operators (Corollary 4.2 in [16]): Denote and let

Then we have

These will be used to estimate the error contributions from the initial values. In order to deal with the convolution terms in we single out the following error estimates. Let

Then we have

The temporal Hölder continuity of the sine and cosine operators, see in [5]:

together with its continuous version:

The equivalence of and , see the proof of Theorem 4.4 in [15]: This uses an inverse inequality, hence our assumption about the quasi-uniformity of the mesh family.

The equivalence of the discrete and continuous norm, see in [1]:

Using the above estimates, one can deduce the following regularity results for the exact solution to our stochastic wave equation and for the exact solution of the finite element approximation .

The proof of this proposition is very similar to the proof of Proposition ? given below and is therefore omitted (see also the proofs of Proposition 3.1 and Lemma 3.3 in [21]).

The next result will be useful in Section 4 when we will deal with the trace formula of the numerical solution.

Let us start with the first estimate of the norm of and consider the expression

Using the fact that and commute, the boundedness of the cosine operator, together with our assumptions on the initial values for the finite element problem, we get

Similarly, one obtains

To estimate the third term, we use , the assumptions on given in , and the equivalence of the norms stated in . First for , we get

because and are bounded. For , we have by

Finally, Ito’s isometry, equations and , and the assumptions on give us

All together, for , one thus obtains

and an application of Gronwall’s lemma give the desired bound for .

The proof for the other bound is done in the same way except for a slight difference in the initial values and that in the integrals is replaced by .

We now prove a Hölder regularity property of the finite element solution. We write, for ,

To estimate the first term we use to get

for . For we note that and that is bounded in the operator norm. Using a similar argument for the second term, we get the following estimate for the first two terms

for . In order to estimate the third term, we use , the assumptions on , and the equivalence of the norms given in . First for , we obtain

For we have, using , , and the fact that is bounded in the operator norm

Similarly we get for the fourth term

To estimate terms five and six we use Ito’s isometry, , , and the assumptions on to get, for ,

and

For we again use that is bounded in the operator norm.

Collecting the above estimates give us the statement about the regularity of the finite element solution.

## 3Mean-square convergence analysis

Recall that the exact solutions to and solve the following equations

where , and

with , , and .

The explicit time discretisation of the finite element solution of the stochastic wave equation using a stochastic trigonometric method with stepsize reads

that is,

where denotes the Wiener increments. Here we thus get an approximation of the exact solution of our finite element problem at the discrete times . Further, a recursion gives

We now look at the error between the numerical and the exact solutions . We follow the same approach as in [23] for parabolic problems, see also [17], and obtain

where we define