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How is the Laplacian defined in multiple dimensions?

In multiple dimensions, the Laplacian Δu\Delta u is defined as the sum of the unmixed second partial derivatives of the scalar function uu with respect to each independent coordinate variable x1,x2,…,xnx_1, x_2, \ldots, x_n. Mathematically, Δu=∂2u∂x12+∂2u∂x22+⋯+∂2u∂xn2\Delta u = \frac{\partial^2 u}{\partial x_1^2} + \frac{\partial^2 u}{\partial x_2^2} + \cdots + \frac{\partial^2 u}{\partial x_n^2}.

Conditions

  • uu is a twice-differentiable scalar function of nn variables.
  • The coordinates x1,…,xnx_1, \ldots, x_n form an orthogonal system (implied by the simple sum).
  • The definition applies to any dimension n≥1n \ge 1.

Reasoning, step by step

  1. Identify the number of dimensions nn.
  2. List the independent variables x1,x2,…,xnx_1, x_2, \ldots, x_n.
  3. For each variable xix_i, compute the second partial derivative ∂2u∂xi2\frac{\partial^2 u}{\partial x_i^2}.
  4. Sum all these second partial derivatives.
  5. Denote the result as Δu\Delta u or ∇2u\nabla^2 u.
  6. Set Δu=0\Delta u = 0 for the Laplace equation in nn dimensions.

Example

The lecturer writes the general formula Δu=∂2u∂x12+∂2u∂x22+⋯+∂2u∂xn2\Delta u = \frac{\partial^2 u}{\partial x_1^2} + \frac{\partial^2 u}{\partial x_2^2} + \cdots + \frac{\partial^2 u}{\partial x_n^2}, explaining that one takes the double derivative on each dimension and sums them up. This generalizes the 2D case where n=2n=2.

Common misconceptions

  • Including mixed partial derivatives (e.g., ∂2u∂x1∂x2\frac{\partial^2 u}{\partial x_1 \partial x_2}) in the sum; the Laplacian only includes pure second derivatives.
  • Assuming the definition changes for non-Cartesian coordinates without adding metric scale factors; the simple sum applies to Cartesian coordinates.
  • Thinking the Laplacian is a vector; it is a scalar quantity.
  • Believing that nn must be 3; the definition holds for any nn.

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