How can I find the scalar field from its gradient?

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Discussion Overview

The discussion revolves around the process of finding a scalar field from its gradient, specifically addressing the challenges and methods involved in integrating a vector field to recover the original scalar function. Participants explore theoretical aspects, mathematical reasoning, and practical examples related to gradients and line integrals.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Technical explanation
  • Debate/contested

Main Points Raised

  • One participant expresses confusion about deriving a scalar field \(\phi\) from its gradient \(\mathbf{F} = \nabla\phi\) and presents an integration approach that leads to incorrect results.
  • Another participant suggests examining the definition of line integrals and highlights that the gradient defines a conservative vector field, linking it to path independence.
  • A different participant provides a specific example of evaluating a line integral to clarify the integration process, correcting a misunderstanding about the evaluation limits.
  • One participant reiterates the initial question and emphasizes the importance of recognizing that the anti-derivative may include a function of another variable, leading to a more general form of \(\phi\).
  • Another participant warns that not all functions of multiple variables are gradients, providing a counterexample to illustrate this point.
  • A participant acknowledges their earlier mistake in treating the integral as a regular integral rather than a line integral and reports successfully applying the advice given by others to resolve their confusion.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the best method for finding the scalar field from its gradient, as there are multiple approaches discussed and some misunderstandings clarified. The discussion includes both agreement on certain mathematical principles and disagreement on specific applications.

Contextual Notes

Participants note limitations in their approaches, such as the dependence on the choice of integration path and the need to consider functions of multiple variables when integrating. There is also mention of the conditions under which gradients can be derived from scalar fields.

Who May Find This Useful

This discussion may be useful for students and practitioners in physics and mathematics who are exploring the relationship between scalar fields and their gradients, particularly in the context of vector calculus and line integrals.

Omri
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Hi,

There is some issue about gradients that disturbs me, so I'd be glad if you could help me figure it out.
Say I have a scalar field [tex]\phi(\mathbf{r})[/tex], that is not yet known. What I know is a function that is the gradient of [tex]\phi[/tex], so that [tex]\mathbf{F}(\mathbf{r}) = \nabla\phi(\mathbf{r})[/tex]. I want to find [tex]\phi[/tex] from [tex]\mathbf{F}[/tex], ignoring the constants of course. What I thought of was:
[tex]d\phi = \sum_{i=1}^{3}\frac{\partial\phi}{\partial x_i} dx_i = \sum_{i=1}^{3} F_i dx_i[/tex]
And therefore:
[tex]\phi = \int d\phi = \int\sum_{i=1}^{3} F_i dx_i[/tex]
But if you try that with the two-dimensional example [tex]\phi = x^2 - xy[/tex], it doesn't work, and gives and gives [tex]x^2 - 2xy[/tex].
Can anyone please explain that to me?

Thanks! :smile:
 
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Look at the definition of a line integral in this case and also note that

curl (grad [tex]\phi[/tex]) =0

so grad [tex]\phi[/tex] defines a vector field which is conservative. Look at how this links to path independence and defines a consistent [tex]\phi[/tex]
 
Hi Omri! :smile:

Suppose your line integral is from (0,0) to (0,y), then from (0,y) to (x,y)

The first part is ∫-x dy = -xy evaluated with x = 0, so it's zero (you thought it was -xy, didn't you? :wink:)

And the second part is ∫(2x - y) dx evaluated with y = y, so it's x2 - xy, as expected. :smile:
 
Omri said:
Hi,

There is some issue about gradients that disturbs me, so I'd be glad if you could help me figure it out.
Say I have a scalar field [tex]\phi(\mathbf{r})[/tex], that is not yet known. What I know is a function that is the gradient of [tex]\phi[/tex], so that [tex]\mathbf{F}(\mathbf{r}) = \nabla\phi(\mathbf{r})[/tex]. I want to find [tex]\phi[/tex] from [tex]\mathbf{F}[/tex], ignoring the constants of course. What I thought of was:
[tex]d\phi = \sum_{i=1}^{3}\frac{\partial\phi}{\partial x_i} dx_i = \sum_{i=1}^{3} F_i dx_i[/tex]
And therefore:
[tex]\phi = \int d\phi = \int\sum_{i=1}^{3} F_i dx_i[/tex]
But if you try that with the two-dimensional example [tex]\phi = x^2 - xy[/tex], it doesn't work, and gives and gives [tex]x^2 - 2xy[/tex].
Can anyone please explain that to me?

Thanks! :smile:
If you are given [itex]\nabla \phi[/itex] and want to find [itex]\phi[/itex] you wouldn't start from a given [itex]\phi[/itex] would you? Well, anyway, if [itex]\phi= x^2- xy[/itex] the [itex]\nabla \phi= (2x- y)\vec{i}- x\vec{j}[/itex]. If you were given [itex](2x- y)\vec{i}- x\vec{j}[/itex] and asked to find [itex]\phi[/itex], its anti-derivative, you would start from the definition
[tex]\nabla \phi= \frac{\partial \phi}{\partial x}\vec{i}+ \frac{\partial \phi}{\partial y}\vec{j}[/tex]
Comparing that general formula to the specific example, we must have
[itex]\frac{\partial \phi}{\partial x}= 2x- y[/itex]
Rememberting that the partial derivative with respect to x treats y like a constant, we can integrate "with respect to x" and get [itex]\phi(x,y)= x^2- xy+ C[/itex] except that, since we are treating y as a constant, that "C" may in fact be a function of y: call it g(y) so we have [itex]\phi(x,y)= x^2- xy+ g(y)[/itex]. Now, differentiate that with respect to y:
[tex]\frac{\partial\phi}{\partial y}= -x+ g'(y)= -x[/itex] so we must have g'(y)= 0. Since g is a function of y only, g' is an ordinary derivative and g'(y)= 0 means g(y)= C, a constant. That is, [itex]\phi(x,y)= x^2- xy+ C[/itex] exactly what we started with except for the constant of integration, C.<br /> <br /> Notice that is exactly what we do to find integrals of "exact differentials" and to solve first order "exact" differential equations.<br /> <br /> <b>Warning</b>, A function of two or three variables is not necessarily equal to a gradient.<br /> For example, if I were to claim that [itex]\nabla\phi(x,y)= 2y\vec{i}+ x\vec{j}[/itex] and try to do the same thing, I would start with <br /> [tex]\frac{\partial\phi}{\partial x}= 2y[/tex] <br /> so [itex]\phi(x,y)= 2xy+ g(y)[/itex]. Differentiating that with respect to y, <br /> [tex]\frac{\partial\phi}{\partial y}= 2y+ g'(y)= x[/tex]<br /> which is impossible!<br /> <br /> The problem is that [itex](2y)_y\ne (x)_x[/itex] so, in ansrivas' terms, [itex]\nabla\cross\nabla f= curl(grad f)\ne 0[/itex] which is impossible.[/tex]
 
Thanks to all of you for your help. My mistake was that I tried to solve the integral as a regular integral and not a line integral, so I didn't pay attention to choosing a curve and taking the limits. I think I did it right now, I took your advice and used the simplest curve - first a line from (0,0) to (x,0) and then a line from (x,0) to (x,y) (or I could go to (0,y) first, it doesn't really matter). Now I got it, thanks!
 

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