Finite difference method derivation PDE

In summary, the finite difference approximation for the Poisson equation uses a central difference scheme and the resulting solution is given by a weighted average of the four neighboring grid points, with a factor of 1/4 in front.
  • #1
fahraynk
186
6

Homework Statement


Which algebraic expressions must be solved when you use finite difference approximation to solve the following Possion equation inside of the square :

$$U_{xx} + U_{yy}=F(x,y)$$[/B]
$$0<x<1$$ $$0<y<1$$
Boundary condition $$U(x,y)=G(x,y)$$

Homework Equations


Central difference approximation
$$U_{xx}=\frac{-2F(x)+F(x+h)+F(x-h)}{h^2}$$
$$U_x=\frac{F(x+h)-F(x-h)}{2h}$$

The Attempt at a Solution


$$U_{xx}+U_{yy} = \frac{1}{h^2}[U(x+h,y)+U(x-h,y)-4U(x,y)+U(x,y+h)+U(x,y-h)]=F(x,y)$$
$$U(x,y)=\frac{1}{4}[U(x+h,y)+U(x-h,y)+U(x,y-h)+U(x,y+h)-h^2F(x,y)]$$

The books solution is
$$\frac{1}{2}[U(i+1,j)+U(i-1,j)+U(i,j+1)+U(i,j-1)]-\frac{h^2}{4}F(x,y)$$

I know why the book changed x,y to i,j... but I don't get why the fraction is 1/2 instead of 1/4 across the entire equation.
 
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  • #2
fahraynk said:

Homework Statement


Which algebraic expressions must be solved when you use finite difference approximation to solve the following Possion equation inside of the square :

$$U_{xx} + U_{yy}=F(x,y)$$[/B]
$$0<x<1$$ $$0<y<1$$
Boundary condition $$U(x,y)=G(x,y)$$

Homework Equations


Central difference approximation
$$U_{xx}=\frac{-2F(x)+F(x+h)+F(x-h)}{h^2}$$
$$U_x=\frac{F(x+h)-F(x-h)}{2h}$$

The Attempt at a Solution


$$U_{xx}+U_{yy} = \frac{1}{h^2}[U(x+h,y)+U(x-h,y)-4U(x,y)+U(x,y+h)+U(x,y-h)]=F(x,y)$$
$$U(x,y)=\frac{1}{4}[U(x+h,y)+U(x-h,y)+U(x,y-h)+U(x,y+h)-h^2F(x,y)]$$

The books solution is
$$\frac{1}{2}[U(i+1,j)+U(i-1,j)+U(i,j+1)+U(i,j-1)]-\frac{h^2}{4}F(x,y)$$

I know why the book changed x,y to i,j... but I don't get why the fraction is 1/2 instead of 1/4 across the entire equation.

I think the book's expression is wrong; your computation is the standard one that appears in numerous textbooks (maybe not yours!) and in many web pages; just Google "laplacian + finite differences".
 

Related to Finite difference method derivation PDE

What is the finite difference method?

The finite difference method is a numerical method used to approximate solutions to partial differential equations (PDEs) by breaking down the domain into a finite number of discrete points and approximating the derivatives at these points using difference equations. It is a powerful tool for solving PDEs that do not have closed-form analytical solutions.

How is the finite difference method derived?

The finite difference method is derived by discretizing the PDE into a set of algebraic equations using Taylor series expansions. The resulting equations are then solved simultaneously to approximate the solution at each discrete point in the domain.

What is the difference between explicit and implicit finite difference methods?

In explicit finite difference methods, the solution at a particular point is calculated solely based on the values at surrounding points in the previous time step. In implicit finite difference methods, the solution at a particular point is calculated based on the values at surrounding points in the current time step, making it more accurate but also more computationally expensive.

What are the advantages of using the finite difference method?

The finite difference method is relatively easy to implement and can be used to solve a wide range of PDEs. It also allows for the use of adaptive grids, meaning that the discretization can be refined in areas where the solution varies significantly, leading to more accurate results.

What are the limitations of the finite difference method?

The finite difference method is limited to regular-shaped domains and may not be suitable for solving PDEs with complex boundary conditions. It can also be computationally expensive for higher-dimensional problems, and the accuracy of the solution may be affected by the size of the grid used.

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