The Heat Kernel

On a finite rod, separation gave a Fourier series. On an infinite rod there are no boundaries to quantise the modes, so the series becomes a Fourier transform. Transforming u_t = \alpha u_{xx} in x turns the PDE into a simple ODE in time for each frequency k:

\hat{u}_t = -\alpha k^2\,\hat{u} \;\Longrightarrow\; \hat{u}(k, t) = \hat{u}(k, 0)\,e^{-\alpha k^2 t}.

Each frequency just decays at rate \alpha k^2 — high frequencies vanishing fastest, the same smoothing we saw on the interval. Transforming back gives the solution as a convolution.

The Gaussian that does everything

The inverse transform of e^{-\alpha k^2 t} is a Gaussian — the heat kernel:

\Phi(x, t) = \frac{1}{\sqrt{4\pi\alpha t}}\,e^{-x^2 / (4\alpha t)}.

It is the temperature from a unit "spike" of heat placed at the origin at t = 0. The solution for any initial profile f is then the kernel convolved with the data — superposing one spreading Gaussian for every starting point:

u(x, t) = \int_{-\infty}^{\infty} \Phi(x - y, t)\,f(y)\,dy.

The kernel reveals two signatures of diffusion. Its width grows like \sqrt{t} (heat spreads, but ever more slowly), while its height falls like 1/\sqrt{t} so the total heat stays constant. And it is positive everywhere for any t > 0: a spike at the origin is instantly felt arbitrarily far away — diffusion has infinite propagation speed, the polar opposite of the wave equation's finite cone.

A spike spreading out

The heat kernel \Phi(x, t) with \alpha = 1. Advance time: the sharp spike slumps into an ever-wider, ever-flatter Gaussian, but the area beneath it (the total heat) never changes. Run t back toward zero and it sharpens toward an infinite spike — the initial point source.