Implementation of Point Source

2020-12-02  本文已影响0人  jjx323

Junxiong Jia, 2020.12.02

Introduction

For solving inverse problems, we often meet point source when tacking adjoint equations, e.g., the following equation
-\Delta u - e^{2q}u = -\frac{1}{\sigma^2}\sum_{j=1}^{K}(w(x)-d_j)\delta(x-x_j) \quad\text{ in }\Omega,
where \{x_j\}_{j=1}^{K} are some measurement points, w(x) is usually some function obtained from forward equation. In the following, we explain that how to implement the point source term on the right-hand side of the above equation.

Measurement Matrix

Usually, for a function in L^2(\Omega) (\Omega is some open bounded domain), it can be decomposed by some fixed orthonormal basis \{ \varphi_{i} \}_{i=1}^{\infty}. In the finite element method, we project functions on a finite number of basis as follow
u(x) \approx \sum_{i=1}^{N}u_i \varphi_i(x).
And once we specify the coefficients \{u_i\}_{i=1}^{N}, the function u(x) will be specified. We introduce the following measurement matrix
S = \left[\begin{matrix} \varphi_1(x_1) & \cdots & \varphi_N(x_1) \\ \vdots & & \vdots \\ \varphi_1(x_K) & \cdots & \varphi_N(x_K) \end{matrix}\right].
The matrix has the following property
\left[\begin{matrix} u(x_1) \\ \vdots \\ u(x_K) \end{matrix}\right] = S \left[\begin{matrix} u_1 \\ \vdots \\ u_N \end{matrix}\right].

Implement of the Point Source

Now we can give specific suggestions for implementing the point source. For discretization, we usually introduce the following quantities
f_k := \int_{\Omega}\sum_{j=1}^{K}(w(x)-d_j)\delta(x-x_j)\varphi_{k}(x)dx
with k = 1,2,\cdots, N. Actually, we can reduce f_k by some simple calculations as follow:
f_k = \sum_{j=1}^{K}(w(x_j)-d_j)\varphi_k(x_j).
With these calculations, we find that
\left[\begin{matrix} f_1 \\ \vdots \\ f_N \end{matrix}\right] = \left[\begin{matrix} \varphi_1(x_1) & \cdots & \varphi_1(x_K) \\ \vdots & & \vdots \\ \varphi_{N}(x_1) & \cdots & \varphi_{N}(x_K) \end{matrix}\right] \left[\begin{matrix} w(x_1)-d_1 \\ \vdots \\ w(x_K)-d_K \end{matrix}\right] = S^T \left[\begin{matrix} w(x_1)-d_1 \\ \vdots \\ w(x_K)-d_K \end{matrix}\right].
Denote d = (d_1,\cdots,d_K)^T, f=(f_1,\cdots,f_N)^T and w=(w_1,\cdots,w_N)^T(coefficients of the function w), we can reduce the above formula as follows:
f = S^T(Sw - d),
which is convenient for implementations by some numerical algorithm, e.g., finite element method.

上一篇下一篇

猜你喜欢

热点阅读