The chain rule for functional differentiation is just the continuum generalisation of the usual chain rule for differentiation of a function of many variables f(y1,y2,…,yN)=f(y), which reads
∂f(y)∂xi(y)=N∑j=1∂yj∂xi∂f∂yj.
The continuum limit amounts to sending the number of variables
N→∞, and defining a new continuous index
r such that
j→rN. Then you just change your notation to agree with the continuous nature of the new index
r, e.g.
xj→X(r),
f(x)=f({xj})→F[X(r)] etc. In our example, this means substituting the above sum over the discrete index
j by an integral over a continuous index, finding the chain rule for functional differentiation:
δF[Y]δX(r)=∫dsδY(s)δX(r)δF[Y]δY(s).
You can get any functional calculus identity you want along the same lines. Just think about what happens in ordinary multivariate calculus with a finite number of variables. Then take the number of variables to infinity.
This post imported from StackExchange Mathematics at 2014-06-16 11:21 (UCT), posted by SE-user Mark Mitchison