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Fourier Analysis - Examples and Constructions

Fourier methods provide explicit solutions to wave equations, diffusion processes, and quantum mechanical problems through separation of variables and transform techniques.

Applications to Wave Equations

ExampleVibrating String Solution

The wave equation for a string of length LL fixed at both ends:

βˆ‚2uβˆ‚t2=c2βˆ‚2uβˆ‚x2,u(0,t)=u(L,t)=0\frac{\partial^2 u}{\partial t^2} = c^2\frac{\partial^2 u}{\partial x^2}, \quad u(0,t) = u(L,t) = 0

Boundary conditions require sine modes:un(x,t)=sin⁑(nΟ€xL)[Ancos⁑(Ο‰nt)+Bnsin⁑(Ο‰nt)]u_n(x,t) = \sin\left(\frac{n\pi x}{L}\right)\left[A_n\cos(\omega_n t) + B_n\sin(\omega_n t)\right]

where Ο‰n=nΟ€c/L\omega_n = n\pi c/L are the normal mode frequencies. The general solution is:

u(x,t)=βˆ‘n=1∞sin⁑(nΟ€xL)[Ancos⁑(Ο‰nt)+Bnsin⁑(Ο‰nt)]u(x,t) = \sum_{n=1}^{\infty}\sin\left(\frac{n\pi x}{L}\right)\left[A_n\cos(\omega_n t) + B_n\sin(\omega_n t)\right]

For initial conditions u(x,0)=f(x)u(x,0) = f(x) and βˆ‚tu(x,0)=g(x)\partial_t u(x,0) = g(x), the coefficients are:

An=2L∫0Lf(x)sin⁑(nΟ€xL)dx,Bn=2Ο‰nL∫0Lg(x)sin⁑(nΟ€xL)dxA_n = \frac{2}{L}\int_0^L f(x)\sin\left(\frac{n\pi x}{L}\right)dx, \quad B_n = \frac{2}{\omega_n L}\int_0^L g(x)\sin\left(\frac{n\pi x}{L}\right)dx

ExampleElectromagnetic Wave Packet

A Gaussian wave packet in free space:

E(x,0)=E0eβˆ’x2/(2Οƒ2)eik0xE(x,0) = E_0 e^{-x^2/(2\sigma^2)}e^{ik_0 x}

evolves according to the wave equation βˆ‚t2E=c2βˆ‚x2E\partial_t^2 E = c^2\partial_x^2 E. Fourier decomposing:

E~(k,0)=E0Οƒ2Ο€eβˆ’(kβˆ’k0)2Οƒ2/2\tilde{E}(k,0) = E_0\sigma\sqrt{2\pi}e^{-(k-k_0)^2\sigma^2/2}

Each component evolves as ei(kxΒ±Ο‰(k)t)e^{i(kx \pm \omega(k)t)} with Ο‰(k)=c∣k∣\omega(k) = c|k|:

E(x,t)=E02∫ei(kxβˆ’ckt)eβˆ’(kβˆ’k0)2Οƒ2/2dkE(x,t) = \frac{E_0}{2}\int e^{i(kx - ckt)}e^{-(k-k_0)^2\sigma^2/2}dk

This gives a wave packet moving at speed cc with carrier wave at k0k_0 and envelope spread Οƒ\sigma.

Spectral Analysis and Power Spectra

DefinitionPower Spectral Density

For a signal f(t)f(t), the power spectral density is:

S(Ο‰)=lim⁑Tβ†’βˆž1Tβˆ£βˆ«βˆ’T/2T/2f(t)eβˆ’iΟ‰tdt∣2S(\omega) = \lim_{T \to \infty}\frac{1}{T}\left|\int_{-T/2}^{T/2} f(t)e^{-i\omega t}dt\right|^2

This measures the power (energy per unit time) at each frequency. For stationary random processes, the Wiener-Khinchin theorem states:

S(Ο‰)=F[R(Ο„)](Ο‰)S(\omega) = \mathcal{F}[R(\tau)](\omega)

where R(Ο„)=⟨f(t)f(t+Ο„)⟩R(\tau) = \langle f(t)f(t+\tau)\rangle is the autocorrelation function.

ExampleWhite Noise

White noise has constant power spectral density S(Ο‰)=S0S(\omega) = S_0 for all frequencies, implying:

R(Ο„)=S0Ξ΄(Ο„)R(\tau) = S_0\delta(\tau)

i.e., the signal is uncorrelated at different times. Physical white noise is band-limited (e.g., Johnson noise in resistors has spectral density S0=4kBTRS_0 = 4k_B T R up to cutoff frequency ∼kBT/ℏ\sim k_B T/\hbar).

Green's Functions via Fourier Methods

ExampleRetarded Green's Function for Wave Equation

The wave equation (βˆ‚t2βˆ’c2βˆ‡2)G(x,t)=Ξ΄3(x)Ξ΄(t)(\partial_t^2 - c^2\nabla^2)G(\mathbf{x},t) = \delta^3(\mathbf{x})\delta(t) with retarded boundary conditions (causality) has Fourier transform:

(βˆ’Ο‰2+c2k2)G~(k,Ο‰)=1(-\omega^2 + c^2k^2)\tilde{G}(\mathbf{k},\omega) = 1

giving:

G~(k,Ο‰)=1c2k2βˆ’Ο‰2βˆ’iΟ΅\tilde{G}(\mathbf{k},\omega) = \frac{1}{c^2k^2 - \omega^2 - i\epsilon}

The iΟ΅i\epsilon prescription ensures the retarded solution. Inverting the Fourier transform:

G(x,t)=Ξ΄(tβˆ’βˆ£x∣/c)4Ο€βˆ£x∣G(\mathbf{x},t) = \frac{\delta(t - |\mathbf{x}|/c)}{4\pi|\mathbf{x}|}

This shows that disturbances propagate on the light cone ∣x∣=ct|\mathbf{x}| = ct.

ExampleDiffusion Kernel

For the diffusion equation βˆ‚tu=Dβˆ‡2u\partial_t u = D\nabla^2 u in R3\mathbb{R}^3 with u(x,0)=Ξ΄3(x)u(\mathbf{x},0) = \delta^3(\mathbf{x}):

βˆ‚tu~=βˆ’Dk2u~β€…β€ŠβŸΉβ€…β€Šu~(k,t)=eβˆ’Dk2t\partial_t\tilde{u} = -Dk^2\tilde{u} \implies \tilde{u}(\mathbf{k},t) = e^{-Dk^2 t}

Inverting (a Gaussian integral):

u(x,t)=1(4Ο€Dt)3/2eβˆ’βˆ£x∣2/(4Dt)u(\mathbf{x},t) = \frac{1}{(4\pi Dt)^{3/2}}e^{-|\mathbf{x}|^2/(4Dt)}

This describes how an initial point concentration spreads out with characteristic radius Dt\sqrt{Dt}, fundamental to Brownian motion and heat conduction.

Fourier Optics

ExampleFraunhofer Diffraction

Light passing through an aperture A(x,y)A(x,y) produces a far-field diffraction pattern. The electric field amplitude in the focal plane of a lens is proportional to the Fourier transform of the aperture:

E(kx,ky)∝∫∫A(x,y)eβˆ’i(kxx+kyy)dx dyE(k_x, k_y) \propto \int\int A(x,y)e^{-i(k_x x + k_y y)}dx\,dy

For a circular aperture of radius aa, this gives the Airy pattern:

I(θ)∝∣J1(kasin⁑θ)kasin⁑θ∣2I(\theta) \propto \left|\frac{J_1(ka\sin\theta)}{ka\sin\theta}\right|^2

where J1J_1 is the Bessel function. The first dark ring occurs at sin⁑θ=1.22λ/(2a)\sin\theta = 1.22\lambda/(2a), defining the Rayleigh criterion for optical resolution.

RemarkWindowed Fourier Transforms

The short-time Fourier transform (STFT) and wavelet transform extend Fourier analysis to non-stationary signals by using localized basis functions, balancing time and frequency resolution. These are essential in image processing, seismology, and time-frequency analysis.

These examples demonstrate how Fourier analysis converts differential equations into algebraic problems and reveals the frequency structure of physical phenomena.