<?xml version="1.0" encoding="UTF-8"?>
<!-- generator="FeedCreator 1.8" -->
<?xml-stylesheet href="https://www.zhuzhugst.com/lib/exe/css.php?s=feed" type="text/css"?>
<rdf:RDF
    xmlns="http://purl.org/rss/1.0/"
    xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
    xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
    xmlns:dc="http://purl.org/dc/elements/1.1/">
    <channel rdf:about="https://www.zhuzhugst.com/feed.php">
        <title>张叶安的博客 - 常微分方程</title>
        <description></description>
        <link>https://www.zhuzhugst.com/</link>
        <image rdf:resource="https://www.zhuzhugst.com/lib/exe/fetch.php?media=logo.png" />
       <dc:date>2026-04-21T20:30:31+00:00</dc:date>
        <items>
            <rdf:Seq>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%8F%98%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654340&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%B8%B8%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654206&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%B8%B8%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%BB%84&amp;rev=1771654487&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E9%AB%98%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654105&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E6%9E%81%E9%99%90%E7%8E%AF%E4%B8%8E%E5%88%86%E6%94%AF&amp;rev=1771493240&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E6%95%B0%E5%80%BC%E8%A7%A3%E6%B3%95&amp;rev=1771493332&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E7%89%B9%E6%AE%8A%E5%87%BD%E6%95%B0&amp;rev=1771493268&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5&amp;rev=1771492674&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E5%BA%94%E7%94%A8&amp;rev=1771493306&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%BB%84&amp;rev=1771654404&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E4%B8%80%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771653913&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E4%B8%80%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E8%A7%A3%E7%9A%84%E5%AD%98%E5%9C%A8%E5%94%AF%E4%B8%80%E6%80%A7&amp;rev=1771654012&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E8%87%AA%E6%B2%BB%E7%B3%BB%E7%BB%9F%E4%B8%8E%E7%9B%B8%E5%B9%B3%E9%9D%A2%E5%88%86%E6%9E%90&amp;rev=1771654605&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:lyapunov%E7%A8%B3%E5%AE%9A%E6%80%A7%E7%90%86%E8%AE%BA&amp;rev=1771493215&amp;do=diff"/>
                <rdf:li rdf:resource="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:sturm-liouville%E8%BE%B9%E5%80%BC%E9%97%AE%E9%A2%98&amp;rev=1771493193&amp;do=diff"/>
            </rdf:Seq>
        </items>
    </channel>
    <image rdf:about="https://www.zhuzhugst.com/lib/exe/fetch.php?media=logo.png">
        <title>张叶安的博客</title>
        <link>https://www.zhuzhugst.com/</link>
        <url>https://www.zhuzhugst.com/lib/exe/fetch.php?media=logo.png</url>
    </image>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%8F%98%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654340&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:12:20+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>变系数线性微分方程</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%8F%98%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654340&amp;do=diff</link>
        <description>第六章 变系数线性微分方程

6.1 引言

当线性微分方程的系数不是常数而是自变量的函数时，方程称为变系数线性微分方程。这类方程通常没有通用的初等解法，但在特定情况下（如某些系数为多项式、幂函数等），可用幂级数解法或特殊函数法求解。$f(x)$$x_0$$f(x) = \sum_{n=0}^{\infty} a_n(x-x_0)^n$$f$$x_0$$y&#039;&#039; + p(x)y&#039; + q(x)y = 0$$p(x), q(x)$$x_0$$x_0$$y = \sum_{n=0}^{\infty} a_n(x-x_0)^n$$a_n$$y&#039;&#039; + y = 0$$x = 0$$y = \sum_{n=0}^{\infty} a_n x^n$$y&#039; = \sum_{n=1}^{\infty} na_n x^{n-1}$$y&#039;&#039; = \sum_{n=2}^{\infty} n(n-1)a_n x^{n-2} = \sum_{n=0}^{\infty} (n+2)(n+1)a_{n+2}x^n$$\sum_{n=0}^{\infty} [(n+2)(n+1)a_{n+2} + a_n]x^n = …</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%B8%B8%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654206&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:10:06+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>常系数线性微分方程</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%B8%B8%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654206&amp;do=diff</link>
        <description>第五章 常系数线性微分方程

5.1 引言

常系数线性微分方程是高阶线性方程中最重要的一类，其理论完善，解法系统，在物理、工程等领域有广泛应用。

$n$ 阶常系数线性微分方程的一般形式为：
$y^{(n)} + a_1y^{(n-1)} + \cdots + a_{n-1}y&#039; + a_ny = f(x)$$a_1, a_2, \ldots, a_n$$f(x)$$y&#039;&#039; + py&#039; + qy = 0$$y = e^{\lambda x}$$\lambda^2e^{\lambda x} + p\lambda e^{\lambda x} + qe^{\lambda x} = 0$$(\lambda^2 + p\lambda + q)e^{\lambda x} = 0$$e^{\lambda x} \neq 0$$\lambda^2 + p\lambda + q = 0$$\lambda_1, \lambda_2$$\lambda_1 \neq \lambda_2 \in \mathbb{R}$$e^{\lambda_1 x}, e^{\lambda_2 x}$$y = C_1e^{\…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%B8%B8%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%BB%84&amp;rev=1771654487&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:14:47+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>常系数线性微分方程组</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%B8%B8%E7%B3%BB%E6%95%B0%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%BB%84&amp;rev=1771654487&amp;do=diff</link>
        <description>第八章 常系数线性微分方程组

8.1 引言

常系数线性微分方程组是线性方程组中最重要的特例，其系数矩阵 $A$ 为常数矩阵。这类方程组有系统的求解方法，应用十分广泛。

标准形式：
$\mathbf{x}&#039; = A\mathbf{x} + \mathbf{f}(t)$

其中 $A$$n \times n$$\mathbf{x}&#039; = A\mathbf{x}$$\mathbf{x} = \mathbf{\xi}e^{\lambda t}$$\mathbf{\xi}$$\lambda$$\lambda\mathbf{\xi}e^{\lambda t} = A\mathbf{\xi}e^{\lambda t}$$A\mathbf{\xi} = \lambda\mathbf{\xi}$$A$$\lambda$$A$$\mathbf{\xi}$$A$$n$$\lambda_1, \ldots, \lambda_n$$\mathbf{\xi}_1, \ldots, \mathbf{\xi}_n$$\mathbf{\varphi}_k = \mathbf{\xi}_k e^{\lambda_k…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E9%AB%98%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654105&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:08:25+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>高阶微分方程</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E9%AB%98%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771654105&amp;do=diff</link>
        <description>第四章 高阶微分方程

4.1 引言

高阶微分方程是指阶数 $n \geq 2$ 的微分方程。在物理学和工程学中，许多问题需要用高阶微分方程来描述，如力学中的Newton第二定律导出的运动方程通常是二阶的。

$n$$F(x, y, y&#039;, y&#039;&#039;, \ldots, y^{(n)}) = 0$$y^{(n)} = f(x, y, y&#039;, \ldots, y^{(n-1)})$$y(x_0) = y_0, y&#039;(x_0) = y_1, \ldots, y^{(n-1)}(x_0) = y_{n-1}$$y^{(n)} = f(x)$$n$$y&#039;&#039;&#039; = e^x$$y&#039;&#039; = e^x + C_1$$y&#039; = e^x + C_1x + C_2$$y = e^x + \frac{C_1x^2}{2} + C_2x + C_3$$F(x, y^{(k)}, y^{(k+1)}, \ldots, y^{(n)}) = 0$$z = y^{(k)}$$n-k$$y$$F(x, y&#039;, y&#039;&#039;) = 0$$z = y&#039;$$y&#039;&#039; = z&#039;$$F(x, z, z&#039;) = 0$$xy&#039;&#039; + y&#039; = …</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E6%9E%81%E9%99%90%E7%8E%AF%E4%B8%8E%E5%88%86%E6%94%AF&amp;rev=1771493240&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-19T09:27:20+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>极限环与分支</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E6%9E%81%E9%99%90%E7%8E%AF%E4%B8%8E%E5%88%86%E6%94%AF&amp;rev=1771493240&amp;do=diff</link>
        <description>第十一章 极限环与分支

11.1 极限环的定义

定义11.1.1（极限环）

对于二维自治系统，孤立的闭轨称为极限环（Limit Cycle）。“孤立”指存在该闭轨的邻域，其中不含其他闭轨。

定义11.1.2（极限环的稳定性）$\Gamma$$t \to +\infty$$\Gamma$$t \to +\infty$$\Gamma$$D \subset \mathbb{R}^2$$\gamma$$D$$D$$\gamma$$\omega$$G$$L_1$$L_2$$L_1$$L_2$$G$$D$$\text{div}\,\mathbf{f} = \frac{\partial P}{\partial x} + \frac{\partial Q}{\partial y}$$D$$\Gamma$$G$$$\oint_\Gamma (Pdy - Qdx) = \iint_G \left(\frac{\partial P}{\partial x} + \frac{\partial Q}{\partial y}\right)dxdy$$$\frac{dx}{dt} = P, \frac{dy…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E6%95%B0%E5%80%BC%E8%A7%A3%E6%B3%95&amp;rev=1771493332&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-19T09:28:52+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>数值解法</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E6%95%B0%E5%80%BC%E8%A7%A3%E6%B3%95&amp;rev=1771493332&amp;do=diff</link>
        <description>第十五章 数值解法

15.1 数值解的基本概念

定义15.1.1（初值问题）

$$\frac{dy}{dx} = f(x, y), \quad y(x_0) = y_0$$

数值解：在离散点 $x_0 &lt; x_1 &lt; \cdots &lt; x_N$ 上求近似值 $y_n \approx y(x_n)$。

步长：$h_n = x_{n+1} - x_n$，常取等步长 $h$。

定义15.1.2（局部截断误差）

单步法 $y_{n+1} = y_n + h\phi(x_n, y_n, h)$ 的局部截断误差：
$$T_{n+1} = y(x_{n+1}) - y(x_n) - h\phi(x_n, y(x_n), h)$$

若 $T_{n+1} = O(h^{p+1})$$p$$$y_{n+1} = y_n + hf(x_n, y_n)$$$T_{n+1} = \frac{h^2}{2}y&#039;&#039;(\xi) = O(h^2)$$O(h)$$$y_{n+1} = y_n + hf(x_{n+1}, y_{n+1})$$$y_{n+1}$$$y_{n+1} = y_n + \frac{…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E7%89%B9%E6%AE%8A%E5%87%BD%E6%95%B0&amp;rev=1771493268&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-19T09:27:48+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>特殊函数</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E7%89%B9%E6%AE%8A%E5%87%BD%E6%95%B0&amp;rev=1771493268&amp;do=diff</link>
        <description>第十三章 特殊函数

13.1 Gamma函数与Beta函数

定义13.1.1（Gamma函数）

对于 $\text{Re}(z) &gt; 0$，Gamma函数定义为：
$$\Gamma(z) = \int_0^{+\infty} t^{z-1}e^{-t}dt$$

基本性质：
1. 递推公式：$\Gamma(z+1) = z\Gamma(z)$
2. 正整数：$\Gamma(n+1) = n!$，$n = 0, 1, 2, \ldots$
3. 特殊值：$\Gamma(1) = 1$，$\Gamma(\frac{1}{2}) = \sqrt{\pi}$
4. 余元公式：$\Gamma(z)\Gamma(1-z) = \frac{\pi}{\sin(\pi z)}$
5. 倍元公式：$\Gamma(2z) = \frac{2^{2z-1}}{\sqrt{\pi}}\Gamma(z)\Gamma(z+\frac{1}{2})$

定义13.1.2（Beta函数）$$B(p, q) = \int_0^1 t^{p-1}(1-t)^{q-1}dt, \quad \text{Re}(p), …</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5&amp;rev=1771492674&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-19T09:17:54+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>微分方程的基本概念</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E5%9F%BA%E6%9C%AC%E6%A6%82%E5%BF%B5&amp;rev=1771492674&amp;do=diff</link>
        <description>第一章 微分方程的基本概念

1.1 引言

微分方程是现代数学中最重要的分支之一，它起源于17世纪牛顿和莱布尼茨创立微积分时期。从物理学中的运动定律到生物学中的种群动力学，从经济学中的增长模型到工程学中的控制系统，微分方程为描述自然现象和社会现象提供了强大的数学工具。$x$$y = y(x)$$F(x, y, y&#039;, y&#039;&#039;, \ldots, y^{(n)}) = 0$$F$$y&#039;, y&#039;&#039;, \ldots, y^{(n)}$$y$$x$$n$$\frac{dy}{dx} = 2x$$y&#039;&#039; + y = 0$$(y&#039;)^2 + xy = \sin x$$\frac{\partial u}{\partial t} = k\frac{\partial^2 u}{\partial x^2}$$\frac{\partial^2 u}{\partial x^2} + \frac{\partial^2 u}{\partial y^2} = 0$$\frac{dy}{dx} = x^2$$y&#039;&#039; + p(x)y&#039; + q(x)y = f(x)$$y^{(4)} + (y&#039;&#039;)^3 = x$$n$$…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E5%BA%94%E7%94%A8&amp;rev=1771493306&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-19T09:28:26+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>微分方程的应用</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E5%BA%94%E7%94%A8&amp;rev=1771493306&amp;do=diff</link>
        <description>第十四章 微分方程的应用

14.1 物理学应用

14.1.1 力学系统

牛顿第二定律：$m\ddot{\mathbf{r}} = \mathbf{F}(\mathbf{r}, \dot{\mathbf{r}}, t)$

谐振子：$m\ddot{x} + kx = 0$，解为 $x = A\cos(\omega t + \varphi)$，$\omega = \sqrt{k/m}$

阻尼振动：$m\ddot{x} + c\dot{x} + kx = 0$

	*  $c &lt; c_{cr} = 2\sqrt{km}$：欠阻尼，衰减振动
	*  $c = c_{cr}$：临界阻尼
	*  $c &gt; c_{cr}$：过阻尼

受迫振动：$m\ddot{x} + c\dot{x} + kx = F_0\cos(\omega t)$

稳态解：$x = A\cos(\omega t - \phi)$，振幅
$$A = \frac{F_0}{\sqrt{(k-m\omega^2)^2 + (c\omega)^2}}$$$\omega = \sqrt{\frac{k}{m} - \frac{…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%BB%84&amp;rev=1771654404&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:13:24+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>线性微分方程组</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E7%BA%BF%E6%80%A7%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%BB%84&amp;rev=1771654404&amp;do=diff</link>
        <description>第七章 线性微分方程组

7.1 引言

在实际问题中，常常需要同时考虑多个相互关联的未知函数，这就导致了微分方程组的研究。线性微分方程组是微分方程理论中最重要的部分之一，它不仅在数学理论上完善，在控制理论、电路分析、生态系统等领域也有广泛应用。$\begin{cases} x_1&#039; = a_{11}(t)x_1 + a_{12}(t)x_2 + \cdots + a_{1n}(t)x_n + f_1(t) \\ x_2&#039; = a_{21}(t)x_1 + a_{22}(t)x_2 + \cdots + a_{2n}(t)x_n + f_2(t) \\ \vdots \\ x_n&#039; = a_{n1}(t)x_1 + a_{n2}(t)x_2 + \cdots + a_{nn}(t)x_n + f_n(t) \end{cases}$$x_i = x_i(t)$$a_{ij}(t)$$f_i(t)$$\mathbf{x} = \begin{pmatrix} x_1 \\ x_2 \\ \vdots \\ x_n \end{pmatrix}, \quad A(t) = \begin{pmat…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E4%B8%80%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771653913&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:05:13+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>一阶微分方程</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E4%B8%80%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B&amp;rev=1771653913&amp;do=diff</link>
        <description>第二章 一阶微分方程

2.1 引言

一阶微分方程是常微分方程中最基础也是最重要的部分，许多实际问题都可以归结为一阶微分方程。本章将系统介绍几类可解析求解的一阶微分方程，包括可分离变量方程、齐次方程、线性方程和恰当方程。$F(x, y, y&#039;) = 0$$y&#039;$$y&#039; = f(x, y)$$\frac{dy}{dx} = f(x)g(y)$$f(x)$$g(y)$$x$$y$$g(y) \neq 0$$\frac{dy}{g(y)} = f(x)dx$$\int \frac{dy}{g(y)} = \int f(x)dx + C$$y_0$$g(y_0) = 0$$y = y_0$$\frac{dy}{dx} = xy$$y \neq 0$$\frac{dy}{y} = xdx$$\ln|y| = \frac{x^2}{2} + C_1$$|y| = e^{C_1}e^{x^2/2}$$C = \pm e^{C_1}$$y = Ce^{x^2/2}$$y = 0$$C = 0$$k &gt; 0$$N_0$$t$$N(t)$$\frac{dN}{dt} = -kN$$\int \frac{…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E4%B8%80%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E8%A7%A3%E7%9A%84%E5%AD%98%E5%9C%A8%E5%94%AF%E4%B8%80%E6%80%A7&amp;rev=1771654012&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:06:52+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>一阶微分方程的解的存在唯一性</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E4%B8%80%E9%98%B6%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B%E7%9A%84%E8%A7%A3%E7%9A%84%E5%AD%98%E5%9C%A8%E5%94%AF%E4%B8%80%E6%80%A7&amp;rev=1771654012&amp;do=diff</link>
        <description>第三章 一阶微分方程的解的存在唯一性

3.1 引言

前两章我们讨论了几类特殊的一阶微分方程的解法。然而，在实际应用中遇到的微分方程往往不能用初等函数表示其解。因此，研究解的存在性和唯一性具有重要的理论意义和实用价值。$y&#039; = y^{2/3}, y(0) = 0$$y = 0$$3y^{1/3} = x + C$$y(0) = 0$$C = 0$$y = (x/3)^3$$a \geq 0$$y_a(x) = \begin{cases} 0, &amp; x \leq a \\ \left(\frac{x-a}{3}\right)^3, &amp; x &gt; a \end{cases}$$y&#039; = y^2, y(0) = 1$$-\frac{1}{y} = x + C$$y = -\frac{1}{x + C}$$y(0) = 1$$C = -1$$y = \frac{1}{1-x}$$x &lt; 1$$x \to 1^-$$y \to +\infty$$x = 1$$y&#039; = \frac{y}{x}, y(0) = 1$$x = 0$$f(x, y)$$D$$L &gt; 0$$(x, y_1), (x, …</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E8%87%AA%E6%B2%BB%E7%B3%BB%E7%BB%9F%E4%B8%8E%E7%9B%B8%E5%B9%B3%E9%9D%A2%E5%88%86%E6%9E%90&amp;rev=1771654605&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-21T06:16:45+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>自治系统与相平面分析</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:%E8%87%AA%E6%B2%BB%E7%B3%BB%E7%BB%9F%E4%B8%8E%E7%9B%B8%E5%B9%B3%E9%9D%A2%E5%88%86%E6%9E%90&amp;rev=1771654605&amp;do=diff</link>
        <description>第九章 自治系统与相平面分析

9.1 自治系统与相空间

定义9.1.1（自治系统）

设 $\mathbf{x} = (x_1, x_2, \ldots, x_n)^T \in \mathbb{R}^n$，$\mathbf{f}: D \subseteq \mathbb{R}^n \to \mathbb{R}^n$ 是连续可微函数。形如
$$\frac{d\mathbf{x}}{dt} = \mathbf{f}(\mathbf{x})$$
的常微分方程组称为自治系统（Autonomous System）或定常系统。与之相对，若右端显含时间 $t$$\frac{d\mathbf{x}}{dt} = \mathbf{f}(t, \mathbf{x})$$\mathbf{x} = \boldsymbol{\varphi}(t)$$\frac{d\mathbf{x}}{dt} = \mathbf{f}(\mathbf{x})$$c$$\mathbf{x} = \boldsymbol{\varphi}(t+c)$$\mathbf{\psi}(t) = \boldsymbol{\varphi}…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:lyapunov%E7%A8%B3%E5%AE%9A%E6%80%A7%E7%90%86%E8%AE%BA&amp;rev=1771493215&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-19T09:26:55+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>lyapunov稳定性理论</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:lyapunov%E7%A8%B3%E5%AE%9A%E6%80%A7%E7%90%86%E8%AE%BA&amp;rev=1771493215&amp;do=diff</link>
        <description>第十章 Lyapunov稳定性理论

10.1 稳定性的定义

考虑自治系统：
$$\frac{d\mathbf{x}}{dt} = \mathbf{f}(\mathbf{x}), \quad \mathbf{x} \in D \subseteq \mathbb{R}^n$$

设 $\mathbf{f}(\mathbf{0}) = \mathbf{0}$，即原点是平衡点。

定义10.1.1（Lyapunov稳定性）

原点称为稳定的（Stable），若对任意 $\varepsilon &gt; 0$，存在 $\delta &gt; 0$，使得当初值满足 $\|\mathbf{x}(0)\| &lt; \delta$ 时，对所有 $t \geq 0$$$\|\mathbf{x}(t)\| &lt; \varepsilon$$$\delta_1 &gt; 0$$\|\mathbf{x}(0)\| &lt; \delta_1$$\lim_{t \to +\infty} \mathbf{x}(t) = \mathbf{0}$$\mathbb{R}^n$$V: D \to \mathbb{R}$$V(\mathbf{0}) =…</description>
    </item>
    <item rdf:about="https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:sturm-liouville%E8%BE%B9%E5%80%BC%E9%97%AE%E9%A2%98&amp;rev=1771493193&amp;do=diff">
        <dc:format>text/html</dc:format>
        <dc:date>2026-02-19T09:26:33+00:00</dc:date>
        <dc:creator>Anonymous (anonymous@undisclosed.example.com)</dc:creator>
        <title>sturm-liouville边值问题</title>
        <link>https://www.zhuzhugst.com/doku.php?id=%E5%B8%B8%E5%BE%AE%E5%88%86%E6%96%B9%E7%A8%8B:sturm-liouville%E8%BE%B9%E5%80%BC%E9%97%AE%E9%A2%98&amp;rev=1771493193&amp;do=diff</link>
        <description>第十二章 Sturm-Liouville边值问题

12.1 Sturm-Liouville问题的形式

定义12.1.1（Sturm-Liouville问题）

二阶线性微分方程的Sturm-Liouville（S-L）形式为：
$$\frac{d}{dx}\left[p(x)\frac{dy}{dx}\right] + [\lambda w(x) - q(x)]y = 0, \quad a &lt; x &lt; b$$

其中：

	*  $p(x) &gt; 0$（通常称为权函数或系数函数）
	*  $w(x) &gt; 0$（权函数）$q(x) \geq 0$$\lambda$$y(a) = y(b) = 0$$$x^2y&#039;&#039; + xy&#039; + (\lambda x^2 - n^2)y = 0$$$x$$xy&#039;&#039; + y&#039; + (\lambda x - \frac{n^2}{x})y = 0$$\frac{d}{dx}\left[x\frac{dy}{dx}\right] + (\lambda x - \frac{n^2}{x})y = 0$$p(x) = x$$w(x) = x$$q(x) = \f…</description>
    </item>
</rdf:RDF>
