一维有限差分法matlab程序
时间: 2023-06-17 21:07:08 浏览: 103
以下是一维有限差分法(Finite Difference Method)的 MATLAB 程序示例:
```matlab
% 输入参数
L = 1; % 区间长度
N = 100; % 离散点数
T = 1; % 时间总长
M = 1000; % 时间离散点数
alpha = 0.1; % 热传导系数
f = @(x) 0; % 初始条件函数
g1 = @(t) 0; % 边界条件函数 1
g2 = @(t) 1; % 边界条件函数 2
% 计算步长
dx = L / (N - 1);
dt = T / (M - 1);
% 初始化矩阵和向量
u = zeros(N, M);
u(:, 1) = f((0:N-1) * dx);
u(1, :) = g1((0:M-1) * dt);
u(N, :) = g2((0:M-1) * dt);
% 构造差分矩阵
r = alpha * dt / (dx^2);
A = (1 + 2 * r) * eye(N) - r * diag(ones(N-1, 1), 1) - r * diag(ones(N-1, 1), -1);
% 迭代求解
for j = 2:M
u(:, j) = A * u(:, j-1);
end
% 绘图
[x, t] = meshgrid((0:N-1)*dx, (0:M-1)*dt);
surf(x, t, u')
xlabel('x')
ylabel('t')
zlabel('u')
```
其中,输入参数包括区间长度 `L`、离散点数 `N`、时间总长 `T`、时间离散点数 `M`、热传导系数 `alpha`、初始条件函数 `f`、边界条件函数 1 `g1` 和边界条件函数 2 `g2`。程序首先计算步长 `dx` 和 `dt`,然后初始化矩阵和向量,并构造差分矩阵 `A`。最后,通过迭代求解差分方程,得到解向量 `u`,并绘制三维图像。
阅读全文
相关推荐

















