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Gard,Curl,Div算子定义及常用公式(一阶导数恒等式)

本文介绍一些Gard,Curl,Div算子定义以及常用的一阶导数恒等式。

一、算子定义

(一)Grad算子定义

对于函数f(x,y,z)f(x,y,z)在三维笛卡尔坐标变量中,梯度是向量场:

grad(f)=f=(x,y,z)f=(fx,fy,fz).\mathrm{grad}(f)=\nabla f=\begin{pmatrix}\frac\partial{\partial x},&\frac\partial{\partial y},&\frac\partial{\partial z}\end{pmatrix}f=(\frac{\partial f}{\partial x},\frac{\partial f}{\partial y},\frac{\partial f}{\partial z}).

Remark: 1. 顾名思义,梯度与函数最快速(正)变化的方向成比例并指向该方向。

  1. 对于一个向量场 A=(A1,..,Am)A =(A_1,.….,A_m) 写成1×n1 ×n 行向量,也称为一阶张量场,梯度或共变导数是n×nn×n雅可比矩阵 :JA=(A)T=(Aixj)ij.:\mathbf{J}_{\mathbf{A}}=(\nabla\mathbf{A})^{\mathrm{T}}=\left(\frac{\partial A_{i}}{\partial x_{j}}\right)_{ij}.
  2. 对于一个任意kk阶张量场AA,梯度grad(A)=(A)Tgrad(A)=(\nabla A)^T是一个k+1k+1阶张量场

(二)Div 算子定义

在笛卡尔坐标中,连续可微矢量场F=(Fx,Fy,Fz)\textbf{F}=(F_x,F_y,F_z)的散度是标量值函数

divF=F=(x,y,z)(Fx,Fy,Fz)=Fxx+Fyy+Fzz.div \textbf{F} =\nabla\cdot \textbf{F}=\left(\frac{\partial}{\partial x},\:\frac{\partial}{\partial y},\:\frac{\partial}{\partial z}\right)\cdot(F_{x},\:F_{y},\:F_{z})=\frac{\partial F_{x}}{\partial x}+\frac{\partial F_{y}}{\partial y}+\frac{\partial F_{z}}{\partial z}.

Remark 顾名思义,散度是向量发散程度的度量.

(三)Curl 算子定义

curlF=×F=(x,y,z)×(Fx,Fy,Fz)=ijkxyzFxFyFz\operatorname{curl}\mathbf{F}=\nabla\times\mathbf{F}=\left(\frac{\partial}{\partial x},\:\frac{\partial}{\partial y},\:\frac{\partial}{\partial z}\right)\times(F_x,\:F_y,\:F_z)=\begin{vmatrix}\mathbf{i}&\mathbf{j}&\mathbf{k}\\\frac{\partial}{\partial x}&\frac{\partial}{\partial y}&\frac{\partial}{\partial z}\\F_x&F_y&F_z\end{vmatrix}

Remark 顾名思义,旋度是附近矢量在圆周方向上趋向的程度的度量。

(四)拉普拉斯算子(调和算子)

Δf=()f=2fx2+2fy2+2fz2.\Delta f=(\nabla\cdot\nabla)f=\frac{\partial^{2}f}{\partial x^{2}}+\frac{\partial^{2}f}{\partial y^{2}}+\frac{\partial^{2}f}{\partial z^{2}}.

二、(A)B(\mathbf{A}\cdot\nabla)\mathbf{B}A(B)\mathbf{A}\cdot(\nabla\mathbf{B})的区别

We note: A=(A1,A2,A3)T,B=(B1,B2,B3)T\mathbf{A}=(A_1,A_2,A_3)^T,\mathbf{B}=(B_1,B_2,B_3)^T, then

算子A=A1x+A2y+A3z\mathbf{A}\cdot\nabla=A_1\frac{\partial}{\partial x}+A_2\frac{\partial}{\partial y}+A_3\frac{\partial}{\partial z},

  • (A)B=(A1B1x+A2B1y+A3B1z,A1B2x+A2B2y+A3B2z,A1B3x+A2B3y+A3B3z)T(\mathbf{A}\cdot\nabla)\mathbf{B}=(A_1\frac{\partial B_1}{\partial x}+A_2\frac{\partial B_1}{\partial y}+A_3\frac{\partial B_1}{\partial z},A_1\frac{\partial B_2}{\partial x}+A_2\frac{\partial B_2}{\partial y}+A_3\frac{\partial B_2}{\partial z},A_1\frac{\partial B_3}{\partial x}+A_2\frac{\partial B_3}{\partial y}+A_3\frac{\partial B_3}{\partial z})^T
  • A(B)=(A1B1x+A2B2x+A3B3x,A1B1y+A2B2y+A3B3y,A1B1z+A2B2z+A3B3z)T\mathbf{A}\cdot(\nabla\mathbf{B})=(A_1\frac{\partial B_1}{\partial x}+A_2\frac{\partial B_2}{\partial x}+A_3\frac{\partial B_3}{\partial x},A_1\frac{\partial B_1}{\partial y}+A_2\frac{\partial B_2}{\partial y}+A_3\frac{\partial B_3}{\partial y},A_1\frac{\partial B_1}{\partial z}+A_2\frac{\partial B_2}{\partial z}+A_3\frac{\partial B_3}{\partial z})^T

三、常用公式(一阶导数恒等式)

For scalar fields ψ,ϕ\psi,\phi and vector fields A,B\mathbf{A}, \mathbf{B}, we have the following derivative identities

Distributive properties

(ψ+ϕ)=ψ+ϕ\nabla(\psi+\phi)=\nabla\psi+\nabla\phi

(A+B)=A+B\nabla(\mathbf{A}+\mathbf{B})=\nabla\mathbf{A}+\nabla\mathbf{B}

(A+B)=A+B\nabla\cdot(\mathbf{A}+\mathbf{B})=\nabla{\cdot}\mathbf{A}+\nabla\cdot\mathbf{B}

×(A+B)=×A+×B\nabla\times\mathbf{(A+B)}=\nabla\times\mathbf{A}+\nabla\times\mathbf{B}

Product rule for multiplication by a scalar

We have the following generalizations of the product rule in single variable calculus.

(ψϕ)=ϕψ+ψϕ\nabla(\psi\phi)=\phi\nabla\psi+\psi\nabla\phi

(ψA)=(ψ)AT+ψA\nabla(\psi\mathbf{A})=(\nabla\psi)\mathbf{A}^\mathbf{T}+\psi\nabla\mathbf{A}

(ψA)=ψA+(ψ)A\nabla\cdot(\psi\mathbf{A})=\psi\:\nabla{\cdot}\mathbf{A}+(\nabla\psi){\cdot}\mathbf{A}

×(ψA)=ψ×A+(ψ)×A\nabla\times(\psi\mathbf{A})=\psi\nabla\times\mathbf{A}+(\nabla\psi)\times\mathbf{A}

Δ(ψϕ)=ψΔϕ+2ψϕ+ϕΔψ\Delta (\psi\phi)=\psi\Delta\phi+2\:\nabla\psi\cdot\nabla\phi+\phi\:\Delta\psi

Quotient rule for division by a scalar

(ψϕ)=ϕψψϕϕ2\nabla\left(\frac\psi\phi\right)=\frac{\phi\nabla\psi-\psi\nabla\phi}{\phi^2}

(Aϕ)=ϕAϕAϕ2\nabla\left(\frac{\mathbf{A}}\phi\right)=\frac{\phi\nabla\mathbf{A}-\nabla\phi\otimes\mathbf{A}}{\phi^2}

(Aϕ)=ϕAϕAϕ2\nabla\cdot\left(\frac{\mathbf{A}}{\phi}\right)=\frac{\phi\nabla\cdot\mathbf{A}-\nabla\phi\cdot\mathbf{A}}{\phi^{2}}

×(Aϕ)=ϕ×Aϕ×Aϕ2\nabla\times\left(\frac{\mathbf{A}}\phi\right)=\frac{\phi\nabla\times\mathbf{A}-\nabla\phi\times\mathbf{A}}{\phi^2}

Dot product rule

(AB)=(A)B+(B)A+A×(×B)+B×(×A)\nabla(\mathbf{A}\cdot\mathbf{B})\:=\:(\mathbf{A}\cdot\nabla)\mathbf{B}\:+\:(\mathbf{B}\cdot\nabla)\mathbf{A}\:+\mathbf{A}\times(\nabla\times\mathbf{B})\:+\mathbf{B}\times(\nabla\times\mathbf{A})

=(B)A+(A)B=(\nabla\mathbf{B})\cdot\mathbf{A}+(\nabla\mathbf{A})\cdot\mathbf{B}

  • See these notes.\mathsf{notes.}As a special case, when A=B,\mathbf{A}=\mathbf{B},

12(A2)=12(AA)=(A)A=(A)A+A×(×A).\frac{1}{2}\nabla\left(|\mathbf{A}|^2\right)\:=\:\frac{1}{2}\nabla\left(\mathbf{A}\cdot\mathbf{A}\right)\:=\:(\nabla\mathbf{A})\cdot\mathbf{A}\:=\:(\mathbf{A}\cdot\nabla)\mathbf{A}\:+\:\mathbf{A}\times(\nabla\times\mathbf{A}).

Notes: 这里 (A)B(\mathbf{A}\cdot\nabla)\mathbf{B}A(B)=((B)A)T\mathbf{A}\cdot(\nabla\mathbf{B})\:=\:((\nabla\mathbf{B})\cdot\mathbf{A})^T的区别在第二部分已经介绍。

Cross product rule

(A×B)=(×A)BA(×B)\nabla\cdot(\mathbf{A}\times\mathbf{B})\:=\:(\nabla{\times}\mathbf{A})\cdot\mathbf{B}\:-\:\mathbf{A}\cdot(\nabla{\times}\mathbf{B})

×(A×B)=A(B)B(A)+(B)A(A)B=(B+B)A(A+A)B=(BAT)(ABT)=(BATABT)A×(×B)=B(AB)(A)B=(B)A(A)B(A×)×B=(B)AA(B)=A×(×B)+(A)BA(B)\begin{aligned}\nabla\times(\mathbf{A}\times\mathbf{B})&=\mathbf{A}(\nabla\mathbf{B})-\mathbf{B}(\nabla\cdot\mathbf{A})+(\mathbf{B}\cdot\nabla)\mathbf{A}-(\mathbf{A}\cdot\nabla)\mathbf{B}\\&=(\nabla\cdot\mathbf{B}+\mathbf{B}\cdot\nabla)\mathbf{A}-(\nabla\cdot\mathbf{A}+\mathbf{A}\cdot\nabla)\mathbf{B}\\&=\nabla\cdot(\mathbf{B}\mathbf{A}^\mathrm{T})-\nabla\cdot(\mathbf{A}\mathbf{B}^\mathrm{T})\\&=\nabla\cdot(\mathbf{B}\mathbf{A}^\mathrm{T}-\mathbf{A}\mathbf{B}^\mathrm{T})\\\mathbf{A}\times(\nabla\times\mathbf{B})&=\nabla\mathbf{B}(\mathbf{A}\cdot\mathbf{B})-(\mathbf{A}\cdot\nabla)\mathbf{B}\\&=(\nabla\mathbf{B})\cdot\mathbf{A}-(\mathbf{A}\cdot\nabla)\mathbf{B}\\\mathbf{(A}\times\nabla)\times\mathbf{B}&=(\nabla\mathbf{B})\cdot\mathbf{A}-\mathbf{A}(\nabla\cdot\mathbf{B})\\&=\mathbf{A}\times(\nabla\times\mathbf{B})+(\mathbf{A}\cdot\nabla)\mathbf{B}-\mathbf{A}(\nabla\cdot\mathbf{B})\end{aligned}

Second derivatives

四、常用公式(重要公式分类汇总)

Gradient

(ψ+ϕ)=ψ+ϕ\nabla(\psi+\phi)=\nabla\psi+\nabla\phi

(ψϕ)=ϕψ+ψϕ\nabla(\psi\phi)=\phi\nabla\psi+\psi\nabla\phi

(ψA)=ψA+ψA\nabla(\psi\mathbf{A})=\nabla\psi\otimes\mathbf{A}+\psi\nabla\mathbf{A}

(AB)=(A)B+(B)A+A×(×B)+B×(×A)\nabla(\mathbf{A}\cdot\mathbf{B})=(\mathbf{A}\cdot\nabla)\mathbf{B}+(\mathbf{B}\cdot\nabla)\mathbf{A}+\mathbf{A}\times(\nabla\times\mathbf{B})+\mathbf{B}\times(\nabla\times\mathbf{A})

Divergence

(A+B)=A+B\nabla\cdot(\mathbf{A}+\mathbf{B})=\nabla\cdot\mathbf{A}+\nabla\cdot\mathbf{B}

(ψA)=ψA+Aψ\nabla\cdot(\psi\mathbf{A})=\psi\nabla\cdot\mathbf{A}+\mathbf{A}\cdot\nabla\psi

(A×B)=(×A)B(×B)A\nabla\cdot(\mathbf{A}\times\mathbf{B})=(\nabla\times\mathbf{A})\cdot\mathbf{B}-(\nabla\times\mathbf{B})\cdot\mathbf{A}

Curl

×(A+B)=×A+×B\nabla\times(\mathbf{A}+\mathbf{B})=\nabla\times\mathbf{A}+\nabla\times\mathbf{B}

×(ψA)=ψ(×A)(A×)ψ=ψ(×A)+(ψ)×A\nabla\times(\psi\mathbf{A})=\psi\left(\nabla\times\mathbf{A}\right)-(\mathbf{A}\times\nabla)\psi=\psi\left(\nabla\times\mathbf{A}\right)+(\nabla\psi)\times\mathbf{A}

×(ψϕ)=ψ×ϕ\nabla\times(\psi\nabla\phi)=\nabla\psi\times\nabla\phi

×(A×B)=A(B)B(A)+(B)A(A)B\nabla\times(\mathbf{A}\times\mathbf{B})=\mathbf{A}\left(\nabla\cdot\mathbf{B}\right)-\mathbf{B}\left(\nabla\cdot\mathbf{A}\right)+(\mathbf{B}\cdot\nabla)\mathbf{A}-(\mathbf{A}\cdot\nabla)\mathbf{B}

Vector dot Del Operator

(A)B=12[(AB)×(A×B)B×(×A)A×(×B)B(A)+A(B)](\mathbf{A}\cdot\nabla)\mathbf{B}=\frac12\left[\nabla(\mathbf{A}\cdot\mathbf{B})-\nabla\times(\mathbf{A}\times\mathbf{B})-\mathbf{B}\times(\nabla\times\mathbf{A})-\mathbf{A}\times(\nabla\times\mathbf{B})-\mathbf{B}(\nabla\cdot\mathbf{A})+\mathbf{A}(\nabla\cdot\mathbf{B})\right]

(A)A=12A2A×(×A)=12A2+(×A)×A(\mathbf{A}\cdot\nabla)\mathbf{A}=\frac12\nabla|\mathbf{A}|^2-\mathbf{A}\times(\nabla\times\mathbf{A})=\frac12\nabla|\mathbf{A}|^2+(\nabla\times\mathbf{A})\times\mathbf{A}

五、常用公式(二阶导数恒等式)

(×A)=0\nabla\cdot(\nabla\times\mathbf{A})=0

×(ψ)=0\nabla\times(\nabla\psi)=\mathbf{0}

×(×A)=(A)ΔA\nabla\times(\nabla\times\mathbf{A})=\nabla(\nabla{\cdot}\mathbf{A})-\Delta\mathbf{A}

(ψ)=Δψ (scalar Laplacian)\nabla\cdot(\nabla\psi)=\Delta\psi\text{ (scalar Laplacian)}

(A)×(×A)=ΔA\nabla\left(\nabla\cdot\mathbf{A}\right)-\nabla\times(\nabla\times\mathbf{A})=\Delta\mathbf{A}

(ϕψ)=ϕΔψ+ϕψ\nabla\cdot(\phi\nabla\psi)=\phi\Delta\psi+\nabla\phi\cdot\nabla\psi

ψΔϕϕΔψ=(ψϕϕψ)\psi\Delta\phi-\phi\Delta\psi=\nabla\cdot(\psi\nabla\phi-\phi\nabla\psi)

Δ(ϕψ)=ϕΔψ+2(ϕ)(ψ)+(Δϕ)ψ\Delta(\phi\psi)=\phi\Delta\psi+2(\nabla\phi)\cdot(\nabla\psi)+\left(\Delta\phi\right)\psi

Δ(ψA)=AΔψ+2(ψ)A+ψΔA\Delta(\psi\mathbf{A})=\mathbf{A}\Delta\psi+2(\nabla\psi\cdot\nabla)\mathbf{A}+\psi\Delta\mathbf{A}

Δ(AB)=AΔBBΔA+2((B)A+B×(×A))\Delta(\mathbf{A}\cdot\mathbf{B})=\mathbf{A}\cdot\Delta\mathbf{B}-\mathbf{B}\cdot\Delta\mathbf{A}+2\nabla\cdot((\mathbf{B}\cdot\nabla)\mathbf{A}+\mathbf{B}\times(\nabla\times\mathbf{A}))

六、常用公式(三阶导数恒等式)

Δ(ψ)=((ψ))=(Δψ)Δ(A)=((A))=(ΔA)Δ(×A)=×(×(×A))=×(ΔA)\begin{aligned} &\bullet\Delta(\nabla\psi)=\nabla(\nabla\cdot(\nabla\psi))=\nabla\left(\Delta\psi\right) \\ &\bullet\left.\Delta(\nabla\cdot\mathbf{A})=\nabla\cdot(\nabla(\nabla\cdot\mathbf{A}))=\nabla\cdot\left(\Delta\mathbf{A}\right)\right. \\ &\bullet\Delta(\nabla\times\mathbf{A})=-\nabla\times(\nabla\times(\nabla\times\mathbf{A}))=\nabla\times\left(\Delta\mathbf{A}\right) \end{aligned}

参考文献

向量微积分恒等式 - HandWiki