Pulling out partial derivatives?

In summary, the conversation discusses the application of a partial derivative operator to a normalized wave function in quantum mechanics. There is a confusion about the use of the operator and the product rule, but it is explained that the extra terms ultimately cancel out and bring the expression back to its original form. The conversation ends with a realization that there is no special property of partial derivatives involved.
  • #1
Cogswell
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I'm reading through the book Quantum Mechanics (Second Edition) by David J. Griffiths and it got to the part about proving that if you normalise a wave function, it stays normalised (Page 13).

That part that I don't get is how they say:

## \dfrac{i \hbar}{2m} \left( \Psi^* \dfrac{\partial^2 \Psi}{\partial x^2} - \dfrac{\partial^2 \Psi^*}{\partial x^2} \Psi \right) = \dfrac{\partial}{\partial x} \left[\dfrac{i \hbar}{2m} \left( \Psi^* \dfrac{\partial \Psi}{\partial x} - \dfrac{\partial \Psi^*}{\partial x} \Psi \right) \right] ##

How can they just pull out a partial operator like that?
Because if you expand it out again it would give you:

## \dfrac{i \hbar}{2m} \left( \dfrac{\partial}{\partial x} \left[ \Psi^* \dfrac{\partial \Psi}{\partial x}\right] - \dfrac{\partial}{\partial x} \left[ \dfrac{\partial \Psi^*}{\partial x} \Psi \right] \right) ##

The operator will be applied to the wrong ## \Psi ## and also won't you need to apply the product rule to is as well?
 
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  • #2
Cogswell said:
and also won't you need to apply the product rule to is as well?

If you do just what you said, you'll see that the extra terms cancel and you'll be back at the original expression. Take out a scrap of paper.
 
  • #3
If you apply the product rule to the first term of ## \dfrac{i \hbar}{2m} \left( \dfrac{\partial}{\partial x} \left[ \Psi^* \dfrac{\partial \Psi}{\partial x}\right] - \dfrac{\partial}{\partial x} \left[ \dfrac{\partial \Psi^*}{\partial x} \Psi \right] \right) ##, you'll get a term ## \dfrac{\partial \Psi}{\partial x}\dfrac{\partial \Psi^*}{\partial x}##, which cancels out with a term ##- \dfrac{\partial \Psi}{\partial x}\dfrac{\partial \Psi^*}{\partial x}## you'll get if you apply the product rule to the second term.
 
  • #4
Haha right, thank you. I thought there was a special property of partial derivatives that I didn't know.
 

FAQ: Pulling out partial derivatives?

1. What is the purpose of calculating partial derivatives?

Partial derivatives are used to determine how a function changes with respect to one of its variables while holding all other variables constant. This allows us to study the rate of change of a function in a specific direction, and is particularly useful in multivariable calculus and optimization problems.

2. How do you calculate a partial derivative?

To calculate a partial derivative, you first identify the variable that you want to differentiate with respect to. Then, you treat all other variables as constants and use standard differentiation rules to find the derivative. This can be done using the limit definition of a derivative or using the chain rule for multivariable functions.

3. What is the difference between a partial derivative and a total derivative?

A partial derivative measures the change in a function in one direction while holding all other variables constant. On the other hand, a total derivative takes into account the changes in all variables at a specific point in the function. In other words, a partial derivative is a special case of a total derivative.

4. Can you give an example of a real-world application of partial derivatives?

Partial derivatives have many applications in physics, engineering, economics, and other fields. One example is in thermodynamics, where partial derivatives are used to calculate the rate of change of temperature with respect to different variables, such as pressure or volume, in thermodynamic processes.

5. How do partial derivatives relate to gradients?

A gradient is a vector that points in the direction of the steepest increase of a function. The components of the gradient are the partial derivatives of the function with respect to each of its variables. In other words, the gradient is a generalization of partial derivatives to multivariable functions and is used to find the direction of maximum increase or decrease of a function.

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