Chain rule with partial derivatives and divergence

In summary, the conversation discusses the concept of divergence and its application to functions with multiple variables. Specifically, the conversation addresses the definition of divergence when one of the variables is a function of the other, as well as the use of notation in calculating the gradient and divergence of a vector function. The concept of continuity and the continuity equation are also mentioned in relation to divergence.
  • #1
bigerst
57
0
say you have a function f(x,y)
[itex]\nabla[/itex]f= [itex]\partial[/itex]f/[itex]\partial[/itex]x + [itex]\partial[/itex]f/[itex]\partial[/itex]y
however when y is a function of x the situation is more complicated
first off [itex]\partial[/itex]f/[itex]\partial[/itex]x = [itex]\partial[/itex]f/[itex]\partial[/itex]x +([itex]\partial[/itex]f/[itex]\partial[/itex]y) ([itex]\partial[/itex]y/[itex]\partial[/itex]x)
( i wrote partial of y to x in case y was dependent on some other variable)
the [itex]\partial[/itex]f/[itex]\partial[/itex]x appears on both sides...what does this mean?do they can cancel? are their values equal?
my best guess is the partial with respect to x on the left side assumes non constant y, whereas the partial on the right wrt x assumes constant y... how would you even show that in notation

now suppose we have a vector function F(x,y(x)), what is then the divergence of F, when we put in the operator [itex]\nabla[/itex] do we assume constant y or non constant y? and in which case does the divergence theorem hold?

thanks
 
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  • #2
When [itex]y = y(x)[/itex], what you're doing is calculating a total derivative instead of a partial. [itex]\nabla f = \hat x \partial_x f + \hat y \partial_y f[/itex] still, but [itex]df/dx = \partial_x f + \partial_y f (dy/dx)[/itex].

Generally, it's easier to deal with parameterized functions instead of [itex]y = y(x)[/itex]. Say [itex]x(t) = t[/itex] and [itex]y(t)[/itex] is some arbitrary function. Really, though, [itex]\nabla[/itex] doesn't make a lot of sense when you're confining yourself to a curve (which is what you're doing when you say [itex]y=y(x)[/itex]. Do you see why? You no longer have the freedom to go in any direction as the definition of the derivative would require.
 
  • #3
You should be careful about your notation.
If you write:
F(x,t)=f(x,y(x,t)) (where I have used "t" as an additional variable),
then you see the answer to your query.

Your doubt about whether to terms are "equal" and somehow cancels, is merely the result of sloppy notation.
 
  • #4
thanks for the replies
i get the total derivative part
say you have function f(x,y,z) where z= g(x,y), how would you define div(f)? I've seen textbooks (griffiths specifically) say divf =[itex]\partial[/itex]fx/[itex]\partial[/itex]x + [itex]\partial[/itex]fy/[itex]\partial[/itex]y+[itex]\partial[/itex]fz/[itex]\partial[/itex]z or something like that. it doesn't make much sense to me.

bigerst
 
  • #5
Like I said, I'm not sure divergence and curl make sense when you're considering a function confined to a surface. Doing that starts to get into a whole bunch of stuff about intrinsic/extrinsic geometry, projections of the derivative, differential geometry in general, and so on. At the least, I suspect that formalism may be more than you were bargaining for.
 
  • #6
bigerst said:
thanks for the replies
i get the total derivative part
say you have function f(x,y,z) where z= g(x,y), how would you define div(f)? I've seen textbooks (griffiths specifically) say divf =[itex]\partial[/itex]fx/[itex]\partial[/itex]x + [itex]\partial[/itex]fy/[itex]\partial[/itex]y+[itex]\partial[/itex]fz/[itex]\partial[/itex]z or something like that. it doesn't make much sense to me.

bigerst
I've never seen any textbook say such a thing. When f is a scalar function of x, y, and z, then
[tex]div f= \nabla f= \frac{\partial f}{\partial x}\vec{i}+ \frac{\partial f}{\partial y}\vec{j}+ \frac{\partial f}{\partial z}\vec{k}[/tex]
Where in Griffiths is the formula you quote and, in particular, what do "fx", "fy", and "fz" mean there? Are they partial derivatives or components of a vector?
 
  • #7
f[itex]_{x}[/itex] means the x compoenent of the vector function f
its on chapter 10 Q8, Griffiths, introduction to electrodynamics 3rd edition
part of problem asks to take the gradient of J(r',t[itex]_{r}[/itex])
∇'J(r',t[itex]_{r}[/itex])
where t[itex]_{r}[/itex] = t - abs(r-r')/c
so treating ∇' = <[itex]\partial[/itex]/[itex]\partial[/itex]x', [itex]\partial[/itex]/[itex]\partial[/itex]y', [itex]\partial[/itex]/[itex]\partial[/itex]z'>
where <> denotes a vector
hence using the chain rule
∇'J(r',t[itex]_{r}[/itex]) = [itex]\partial[/itex]Jx/[itex]\partial[/itex]x' +[itex]\partial[/itex]Jy/[itex]\partial[/itex]y' +[itex]\partial[/itex]Jz/[itex]\partial[/itex]z' -(1/c)([itex]\partial[/itex]J/[itex]\partial[/itex]t[itex]_{r}[/itex])∇(abs(r-r'))
the answer, however is ∇'J(r',t[itex]_{r}[/itex])=-[itex]\partial[/itex]p/[itex]\partial[/itex]t -(1/c)([itex]\partial[/itex]J/[itex]\partial[/itex]t[itex]_{r}[/itex])∇(abs(r-r'))
where i believe the author makes use of the continuity equation that states
[itex]\nabla[/itex]J= - [itex]\partial[/itex]p/[itex]\partial[/itex]t by arguing [itex]\partial[/itex]Jx/[itex]\partial[/itex]x' +[itex]\partial[/itex]Jy/[itex]\partial[/itex]y' +[itex]\partial[/itex]Jz/[itex]\partial[/itex]z' = ∇ J
this made no sense to me as i thought wasnt ∇'J(r',t[itex]_{r}[/itex]) already the divergence?
hence i think either the definition of divergence is different somehow
perhaps the div(J) on the right side means taking the time to be constant while the div on the left side is more of a "absolute divergence?" then how would the divergence theorem hold in that case? define a new G(r',r,t)=J(r',t[itex]_{r}[/itex]) ?
thanks

bigerst
 

FAQ: Chain rule with partial derivatives and divergence

1. What is the chain rule with partial derivatives?

The chain rule with partial derivatives is a mathematical rule used in multivariable calculus to find the derivative of a function composed of multiple variables. It allows for the calculation of the partial derivative of a function with respect to one variable while holding all other variables constant.

2. Why is the chain rule important in multivariable calculus?

The chain rule is important because it allows for the calculation of derivatives in complex functions with multiple variables. It is essential in fields such as physics, engineering, and economics where many real-world problems involve multiple variables.

3. How is the chain rule applied in practice?

The chain rule is applied by first identifying the innermost function in a composed function and finding its derivative. Then, the derivatives of the outer functions are calculated and multiplied together, with each derivative being evaluated at the corresponding inner function. This process is repeated until the derivative of the entire function is obtained.

4. What is the relationship between the chain rule and partial derivatives?

The chain rule is closely related to partial derivatives as it allows for the calculation of partial derivatives in multivariable functions. It is often used in conjunction with the partial derivative notation to represent the dependency on multiple variables in a function.

5. How is the chain rule used in the concept of divergence?

In the concept of divergence, the chain rule is used to calculate the derivative of a vector field with respect to a scalar function. It allows for the decomposition of a vector field into scalar functions, which is useful in the analysis of fluid flow, electromagnetism, and other physical phenomena.

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