散度（divergence）係向量微積分嘅一個概念，適用喺多變量向量場函數。一個多變量向量函數嘅散度係一個多變量純量函數。散度嘅意思係，對於投射喺一個座標平面嘅多變量純量函數，由佢喺每一個點嘅散度可以知道喺嗰一點附近嘅區域，個向量場有幾傾向（即係睇落有幾似）係由嗰一點作為源頭而散開。散度通常寫做 ∇ ⋅ u {\displaystyle \nabla \cdot \mathbf {u} } 。
散度嘅定義要用到Nabla 算子嚟表示。對於一個多變量向量函數 u ( x 1 , . . . , x n ) = ∑ i = 1 n e → i u i ( x 1 , . . . , x n ) {\displaystyle \mathbf {u} (x_{1},...,x_{n})=\sum _{i=1}^{n}{\vec {e}}_{i}u_{i}(x_{1},...,x_{n})} ，佢嘅散度係：
∇ ⋅ u = ( ∑ i = 1 n e → i ∂ ∂ x i ) ⋅ ( ∑ i = 1 n e → i u i ) = ∑ i = 1 n ∂ u i ∂ x i {\displaystyle \nabla \cdot \mathbf {u} =(\sum _{i=1}^{n}{\vec {e}}_{i}{\frac {\partial }{\partial x_{i}}})\cdot (\sum _{i=1}^{n}{\vec {e}}_{i}u_{i})=\sum _{i=1}^{n}{\frac {\partial u_{i}}{\partial x_{i}}}}
譬如喺 R 3 {\displaystyle \mathbb {R} ^{3}} 平面上，散度係：
∇ ⋅ u = ( i ∂ ∂ x + j ∂ ∂ y + k ∂ ∂ z ) ⋅ ( u 1 i + u 2 j + u 3 k ) = ∂ u 1 ∂ x + ∂ u 2 ∂ y + ∂ u 3 ∂ z {\displaystyle \nabla \cdot \mathbf {u} =(\mathbf {i} {\frac {\partial }{\partial x}}+\mathbf {j} {\frac {\partial }{\partial y}}+\mathbf {k} {\frac {\partial }{\partial z}})\cdot (u_{1}\mathbf {i} +u_{2}\mathbf {j} +u_{3}\mathbf {k} )={\frac {\partial u_{1}}{\partial x}}+{\frac {\partial u_{2}}{\partial y}}+{\frac {\partial u_{3}}{\partial z}}}
想搞清楚散度嘅概念，去 https://www.youtube.com/watch?v=ThxMvNPMitE&t=226s 睇下。
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，佢嘅散度係：
平面上，散度係：
