Velocity correlation functions

AI Thread Summary
The discussion revolves around a challenge in deriving the relationship between the mean square displacement and the velocity correlation function in non-equilibrium statistical mechanics. The equation <x^{2}> = ∫^{t}_{0}ds_{1}∫^{t}_{0}ds_{2}<v(s_{1})v(s_{2})> is presented, and the user seeks to show that ∂<x^{2}>/∂t = 2 ∫^{t}_{0}ds<v(s)v(t)>. The fundamental theorem of calculus is suggested as a key to the solution, with a focus on applying the product rule to the integrals involved. The response emphasizes using total derivatives with respect to t to facilitate the calculation. This discussion highlights the complexities of applying calculus in statistical mechanics.
thrillhouse86
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Hi, I am going through Non Equilibrium Statistical Mechanics by Zwanzig and I can't follow, the step below:

I have the equation:
<br /> &lt;x^{2}&gt; = \int^{t}_{0}ds_{1}\int^{t}_{0}ds_{2}&lt;v(s_{1})v(s_{2})&gt;<br />

I can't show that:
<br /> \frac{\partial &lt;x^{2}&gt;}{\partial t} = 2 \int^{t}_{0}ds&lt;v(s)v(t)&gt;<br />

I'm sure that the answer lies with that fundamental theorem of calculus, but I can't show it. For one thing, do I apply the product rule to the two integrals above ?

Thanks
 
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call the upper indices (which are now both called t) a(t)=t and b(t)=t. Then use that the total derivative (well, total at least with respect to t) is d/dt=da/dt d/da+db/dt d/db.
 
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