当前位置: 首页 > news >正文

zju博士资格考试考前复习(微分方程方向)pde 部分

zju博士资格考试考前复习(微分方程方向)pde 部分

资料来源:

22-23《偏微分方程》课程笔记

Evens PDE


1. 一阶线性方程

定义

关于 \(u\) 的一阶线性方程

\[u_t + b\cdot Du = 0, (x,t)\in \mathbb{R}^n\times (0,\infty) \]

通解:特征线法

固定 \((x,t)\),令 \(z(s) := u(x+sb, t+s)\),则

\[z'(s) = b\cdot Du(x+sb, t+s)+u_t(x+sb,t+s)=0 \]

因此 \(z(s) = C\)。即

\[u(x, t) = F(x - tb), F\in C^1(\mathbb R^n). \]

齐次初值问题

\[\begin{aligned} u_t + b\cdot Du = 0 ,& \text{in }\mathbb{R}^n\times (0,\infty) \\ u = g ,& \text{on }\mathbb{R}^n \times \{0\} \end{aligned} \]

解就是 \(u(x, t) = g(x - tb)\)

非齐次初值问题

\[\begin{aligned} u_t + b\cdot Du = &f, \text{in }\mathbb{R}^n\times (0,\infty) \\ u = &g, \text{on }\mathbb{R}^n\times \{0\} \\ \end{aligned} \]

齐次化方法

固定 \((x,t)\),令 \(z(s) = u(x+sb, t+s)\),则

\[z'(s) = b\cdot Du(x+sb, t+s) + u_t(x+sb, t+s) = f(x+sb, t+s) \]

因此

\[\begin{aligned} & u(x,t) - g(x-tb) \\ = & z(0) - z(-t) \\ = & \int_{-t}^0 z'(s) ds \\ = & \int_{-t}^0 f(x+sb,t+s)ds \\ = & \int_0^t f(x+(s-t)b,s)ds \\ \end{aligned} \]

\[u(x,t) = g(x-tb) + \int_0^t f(x+(s-t)b, s)ds. \]

2. 一阶拟线性方程

定义

\[u_t + a(u)u_x = 0, u(0,x) = \varphi(x). \]

解法

\(x = x(t)\) 是初值问题 \(x' = a(u(t, x(t)))\) 的解,则

\[U(t) = u(t, x(t)) \]

沿曲线 \(x = x(t)\) 为常数。

由初值条件得 \(u(0, x) = \varphi(x)\),所以

\[x(t) = x(0) + a(u(0))t, U(t) = U(0). \]

\(\alpha := x(0)\),则 \(U(0) = u(0, x(0)) = \varphi(\alpha))\)。故 \(u\) 满足参数方程

\[x(t) = \alpha + a(\varphi(\alpha))t, u(t, x(t)) = \varphi(\alpha). \]

特别地,当 \(\varphi\)\(\mathbb R\) 上单增时,上述初值问题有唯一解。

3. 一维波动方程 - 初值问题

齐次初值问题

\[\begin{aligned} &u_{tt} - c^2u_{xx} = 0,\\ t=0: &u = \varphi, u_t = \psi. \\ \end{aligned} \]

D‘Alembert 公式

将方程重写为

\[\left(\frac{\partial}{\partial t} - c\frac{\partial}{\partial x}\right)\left(\frac{\partial}{\partial t} + c\frac{\partial}{\partial x}\right)u = 0. \]

\(\xi = x-ct, \eta = x+ct\),则

\[\frac{\partial^2 u}{\partial\xi\partial\eta} = 0. \]

所以 \(u = F(\xi) + G(\eta) = F(x-ct) + G(x+ct)\)

根据初值条件,有

\[\varphi(x) = F(x) + G(x), \psi(x) = -cF'(x) + cG'(x). \]

联立解得

\[\begin{aligned} F(x) &= \frac 12 \varphi(x) + \frac 1{2c}\int_0^x \psi(s)ds + C, \\ G(x) &= \frac 12 \varphi(x) - \frac 1{2c}\int_0^x \psi(s)ds - C. \\ \end{aligned} \]

所以

\[u(t, x) = \frac 12[\varphi(x+ct) + \varphi(x-ct)] + \frac 1{2c}\int_{x-ct}^{x+ct}\psi(s)ds. \]

非齐次初值问题

\[\begin{aligned} &u_{tt} - c^2u_{xx} = f,\\ t=0: &u = 0, u_t = 0. \\ \end{aligned} \]

齐次化

\(w(t,x;\tau)\) 是齐次问题

\[\begin{aligned} &w_{tt} - c^2w_{xx} = 0,\\ t=0: &w = 0, w_t = f(\tau, x). \\ \end{aligned} \]

\[u(t, x) = \int_0^t w(t, x; \tau)d\tau. \]

注:此原理同样适用于边值问题和初边值问题。

叠加原理

\[\begin{aligned} &u_{tt} - c^2u_{xx} = f,\\ t=0: &u = \varphi, u_t = \psi. \\ \end{aligned} \]

将这个方程分解为两个子方程(只有右端项和只有初值),然后加权相加。

注:叠加原理适用于所有线性方程。

4. 一维波动方程 - 边值问题

Dirichlet 初边值问题

\[\begin{aligned} &u_{tt} - c^2u_{xx} = f,\\ t=0: &u = \varphi, u_t = \psi, \\ x=0: &u = g, \\ x=L: &u = h. \\ \end{aligned} \]

Step1:叠加原理分解为三个子问题

\[I_1: \begin{aligned} &u_{tt} - c^2u_{xx} = f,\\ t=0: &u = 0, u_t = 0, \\ x=0: &u = 0, \\ x=L: &u = 0. \\ \end{aligned} \]

\[I_2: \begin{aligned} &u_{tt} - c^2u_{xx} = 0,\\ t=0: &u = \varphi, u_t = \psi, \\ x=0: &u = 0, \\ x=L: &u = 0. \\ \end{aligned} \]

\[I_3: \begin{aligned} &u_{tt} - c^2u_{xx} = 0,\\ t=0: &u = 0, u_t = 0, \\ x=0: &u = g, \\ x=L: &u = h. \\ \end{aligned} \]

Step2:统一转化为 \(I_2\) 的形式

\(I_1\) 可通过齐次化原理转化为 \(I_2\)

\(I_3\),令 \(\phi(t,x) = g(t) - \frac xL[h(t) - g(t)]\),则 \(\phi\) 满足边值条件。再令 \(v = u - \phi\),则 \(v\) 满足

\[I_4: \begin{aligned} &v_{tt} - c^2v_{xx} = -g'' - \frac xL[h'' - h''],\\ t=0: &v = -g(0) - \frac xL[h(0)-g(0)], v_t = -g'(0) - \frac xL[h'(0)-g'(0)] , \\ x=0: &v = 0, \\ x=L: &v = 0. \\ \end{aligned} \]

进而通过叠加原理再次拆分即可。

Step3:分离变量法解 \(I_2\)

假设 \(u(t, x) = T(t)X(x)\),则

\[T''(t)X(x) - c^2T(t)X''(x) = 0, \]

\[\frac{T''(t)}{c^2T(t)} = \frac{X''(x)}{X(x)}. \]

两端变量不同但相等,故只能为常数。设为 \(-\lambda\)\(\lambda > 0\)。因此

\[T''(t) + c^2\lambda T(t) = 0, X''(x) + \lambda X(x) = 0. \]

问题转化为边值问题

\[\begin{aligned} X''(x) + \lambda X(x) = 0; \\ X(0) = 0, X(L) = 0. \end{aligned} \]

上述方程有非平凡解必须有 \(\sqrt \lambda L = k\pi, k\in \mathbb N\)。即

\[X_k(x) = C_k\sin \frac{k\pi}{L}x. \]

\[T_k(t) = A_k\cos\frac{ck\pi}{L}t + B_k\sin\frac{ck\pi}{L}t. \]

所以(系数可合并)

\[u_k(t,x) = T_k(t)X_k(x) = (A_k\cos\frac{ck\pi}{L}t + B_k\sin\frac{ck\pi}{L}t)\sin\frac{k\pi}{L}x. \]

原边值问题的通解

\[u(t, x) = \sum_{k=1}^\infty u_k(t, x) = \sum_{k=1}^\infty(A_k\cos\frac{ck\pi}Lt + B_k\sin\frac{ck\pi}Lt)\sin\frac{k\pi}Lx. \]

根据初值条件

\[\begin{aligned} \varphi(x) &= \sum_{k=1}^\infty A_k\sin\frac{k\pi}Lx; \\ \psi(x) &= \sum_{k=1}^\infty \frac{ck\pi}LB_k\sin\frac{k\pi}Lx. \\ \end{aligned} \]

因此 \(A_k, B_k\) 就是 Fourier 系数

\[\begin{aligned} A_k &= \frac 2L\int_0^L \varphi(s)\sin\frac{k\pi s}Lds; \\ B_k &= \frac 2{ck\pi}\int_0^L \psi(s)\sin\frac{k\pi s}Lds. \\ \end{aligned} \]

5. Fourier 变换

变换公式

\[\mathcal F[f](\xi) = \int_\mathbb R f(x)e^{-i\xi x}dx, \mathcal F^{-1}[g](x) = \frac 1{2\pi}\int_\mathbb Rg(\xi)e^{ix\xi}d\xi. \]

性质

  • \(\mathcal F^{-1}[\mathcal F[f]] = f\)
  • \(\mathcal F[c_1f_1 + c_2f_2] = c_1\mathcal F[f_1] + c_2\mathcal F[f_2]\)
  • \(\mathcal F[f_1 * f_2] = \mathcal F[f_1]\mathcal F[f_2]\)
  • \(\mathcal F^{-1}[g_1 * g_2] = 2\pi\mathcal F^{-1}[g_1]\mathcal F^{-1}[g_2]\)
  • \(\mathcal F[f_1f_2] = \frac 1{2\pi}\mathcal F[f_1]*\mathcal F[f_2]\)
  • \(\mathcal F[f'] = i\xi\mathcal F[f]\)
  • \(\mathcal F[-ixf] = (\mathcal F[f])'\)

6. 一维热方程 - 初值问题

齐次初值问题

\[\begin{aligned} u_t &= c^2u_{xx}; \\ u|_{t=0} &= \varphi. \\ \end{aligned} \]

Fourier 变换法

\(t\) 视为参数,关于 \(x\) 作 Fourier 变换。记 \(\tilde u(t, \xi) = \mathcal F[u], \tilde \varphi(\xi) := \mathcal F[\varphi]\),则

\[\mathcal F[u_{xx}] = -c^2\xi^2\tilde u. \]

因此

\[\tilde u_t = -c^2\xi^2\tilde u, \tilde u(0,\xi) = \tilde\varphi. \]

直接解得

\[\tilde u(t, \xi) = \tilde\varphi(\xi)e^{-c^2\xi^2t}. \]

两边关于 \(\xi\) 作 Fourier 逆变换,得

\[u = \mathcal F^{-1}[\tilde u] = \mathcal F^{-1}[\tilde\varphi e^{-c^2\xi^2t}]. \]

\[\mathcal F^{-1}[e^{-c^2\xi^2t}] = \frac 1{2\pi}\int_{\mathbb R}e^{-(c^2\xi^2t-i\xi x)}d\xi = \frac 1{2\pi}e^{-\frac{x^2}{4c^2t}}\int_{\mathbb R}e^{-c^2t(\xi-\frac{ix}{2c^2t})^2}d\xi = \frac 1{2\pi}e^{-\frac{x^2}{4c^2t}}\frac{\sqrt\pi}{c\sqrt t} = \frac 1{2c\sqrt\pi t}e^{-\frac{x^2}{4c^2t}}. \]

所以

\[u(t, x) = \mathcal F^{-1}[\tilde\varphi]*\mathcal F^{-1}[e^{-c^2\xi^2t}] = \varphi * \left(\frac 1{2c\sqrt\pi t}e^{-\frac{x^2}{4c^2t}}\right) = \frac 1{2c\sqrt\pi t}\int_{\mathbb R}\varphi(\xi)e^{-\frac{(x-\xi)^2}{4c^2t}}d\xi. \]

非齐次初值问题

\[\begin{aligned} u_t &= c^2u_{xx} + f, \\ u|_{t=0} &= \varphi. \end{aligned} \]

由齐次化原理,当 \(\varphi=0\) 时方程的解为

\[u(t,x) = \int_0^t w(t,x;\tau)d\tau = \frac 1{2c\sqrt\pi t}\int_0^t\int_\mathbb R \frac{f(\tau,\xi)}{\sqrt{t-\tau}}e^{-\frac{(x-\xi)^2}{4c^2(t-\tau)}}d\xi d\tau. \]

由叠加原理,原方程的解

\[u(t,x) = \frac 1{2c\sqrt\pi t}\int_{\mathbb R}\varphi(\xi)e^{-\frac{(x-\xi)^2}{4c^2t}}d\xi + \frac 1{2c\sqrt\pi t}\int_0^t\int_\mathbb R \frac{f(\tau,\xi)}{\sqrt{t-\tau}}e^{-\frac{(x-\xi)^2}{4c^2(t-\tau)}}d\xi d\tau. \]

7. 一维热方程 - 初边值问题

齐次初边值问题

\[\begin{aligned} u_t &= c^2u_{xx}, \\ t = 0: & u = \varphi(x), \\ x = 0: & u = 0, \\ x = L: & u_x + \sigma u = 0. \\ \end{aligned} \]

分离变量法

假设 \(u(t, x) = T(t)X(x)\),则

\[X(x)T'(t) = c^2X''(x)T(t), \]

\[\frac{T'(t)}{c^2T(t)} = \frac{X''(x)}{X(x)} = -\lambda. \]

所以

\[T'(t) + \lambda c^2T(t) = 0, X''(x) + \lambda c^2X(x) = 0. \]

由边值条件得 \(X(0) = 0, X'(L) + \sigma X(L) = 0\)

\(\lambda > 0\) 时,该 ODE 有非平凡解

\[X(x) = A\cos\sqrt\lambda x + B\sin\sqrt\lambda x. \]

代入边值

\[X(0) = A = 0, X'(L) + \sigma X(L) = B(\sqrt\lambda\cos\sqrt\lambda L + \sigma\sin\sqrt\lambda L) = 0. \]

\(\lambda\) 必须满足方程 \(\sqrt\lambda\cos\sqrt\lambda L + \sigma\sin\sqrt\lambda L = 0\)。即 \(\tan\sqrt\lambda L = -\frac{\sqrt\lambda}{\sigma}\)。该方程有无穷多个正解 \(\lambda_k(k\in \mathbb N)\)

因此关于 \(X\) 的 ODE 有一列特解

\[X_k(x) = B_k\sin\sqrt{\lambda_k}x. \]

所以

\[u(t,x) = \sum_{k=1}^\infty u_k(t,x) = \sum_{k=1}^\infty A_ke^{-c^2\lambda_k t}\sin\sqrt{\lambda_k}x. \]

因为 \(u(0, x) = \varphi(x)\),所以 \(\varphi(x) = \sum_{k=1}^\infty A_k\sin \sqrt{\lambda_k}x\)

可证明特征函数系 \(\{\sin\sqrt{\lambda_k}x\}\)\([0,L]\) 上正交。在 \(\varphi(x)\) 两边同时乘 \(\sin\sqrt{\lambda_k}x\) 并在 \([0,L]\) 上积分得

\[\int_0^L \sin\sqrt{\lambda_k}x\sum_{j=1}^\infty A_j\sin \sqrt{\lambda_j}xdx = A_k\int_0^L \sin^2\sqrt{\lambda_k}xdx = \int_0^L \varphi(x)\sin\sqrt{\lambda_k}xdx. \]

\[\int_0^L\sin^2\sqrt{\lambda_k}xdx = \int_0^L \frac{1-\cos 2\sqrt{\lambda_k}x}{2}dx = \frac L2 - \frac{\sin 2\sqrt{\lambda_k}L}{4\sqrt{\lambda_k}} = \frac L2 - \frac{\sigma}{2(\sigma^2 + \lambda_k)}. \]

所以

\[u(t, x) = \sum_{k=1}^\infty \frac{2(\sigma^2 + \lambda_k)}{L(\sigma^2 + \lambda_k)-\sigma}e^{-c^2\lambda_kt}\sin\sqrt{\lambda_k}t\int_0^L\varphi(s)\sin\sqrt{\lambda_k}sds. \]

http://www.hskmm.com/?act=detail&tid=24854

相关文章:

  • 完整教程:OS9.【Linux】基本权限(下)
  • arEPRP and arEHS
  • 图论
  • 价值原语博弈:AI元人文的伦理架构探索
  • 文件的物理结构I
  • VR/AR 显示瓶颈将破!铁电液晶技巧迎来关键突破
  • 博客园-awescnb插件-geek皮肤异常问题修复
  • 国庆 Day1 强基化学
  • 2025 年 AI 应用数据泄露防范:以“流式网关”为中枢的链路化治理与合规映射
  • Alexa进入自主时代:AI技术新突破
  • 入门AJAX——XMLHttpRequest(Get) - 教程
  • ROM和RAM
  • 深入解析:C#学习26天:内存优化的几种方法
  • 整理数据制作 直方图,箱须图,概率密度估计(KDE)图
  • UCosIII 在 Tang Nano 20K 的 SparrowRV 软核移植
  • SIP抓包工具 SIP抓包 SIP抓包
  • 2025声级计厂家最新权威推荐排行榜单! 数字声级计,精密声级计,防爆声级计,手持式声级计,剂量声级计公司推荐!
  • python中使用高并发分布式队列库celery的那些坑 - 指南
  • 在AI技术唾手可得的时代,挖掘新需求成为核心竞争力——某知名计算机控制AI框架需求洞察
  • Codeforces Round 1040 (Div. 1)
  • 2025十一集训——Day3做题
  • 目标检测任务的评估指标P-R曲线 - 指南
  • abc426 题解
  • 运行npp并打开实时双向同步的今日日记纯文本文档 2025年10月5日
  • 完整教程:python学习打卡day43
  • mac 下修改本机hosts
  • 2025测振仪厂家最新企业品牌推荐排行榜,自动诊断测振仪,防爆测振仪,智能测振仪,诊断故障测振仪推荐!
  • 【JNI】JNI环境搭建
  • CS自学笔记
  • vue: 报错: vue ResizeObserver loop completed with undelivered notifications.