Functional derivative in $n$- dimensions using vector calculus

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Let F(g(f)) be a functional that sends functions of a vector variable ( from n -dimensional vector space) to R.

g is some function of scalar valued functions f .

It can be shown using vector calculus that the first (order) functional derivative $\partial F$ is equal to the partial derivative of g with respect to f minus the divergence of the gradient of g .

https://en.m.wikipedia.org/wiki/Functional_derivative

$F=\int f ^{4} dx^{4}$

$\partial F= \frac{\partial{g}}{\partial{f}} - \nabla \cdot\nabla g(f)$

Divergence of the gradient should be equal to 4 , and if I plug in the above functional into the formula on wiki it should work just as well for 4d and I get the result :$ \partial F=−12f^{3}$

Can I just plug this into the formula again and get the second derivative of the functional?