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
The wave equation for a string of length fixed at both ends:
Boundary conditions require sine modes:
where are the normal mode frequencies. The general solution is:
For initial conditions and , the coefficients are:
A Gaussian wave packet in free space:
evolves according to the wave equation . Fourier decomposing:
Each component evolves as with :
This gives a wave packet moving at speed with carrier wave at and envelope spread .
Spectral Analysis and Power Spectra
For a signal , the power spectral density is:
This measures the power (energy per unit time) at each frequency. For stationary random processes, the Wiener-Khinchin theorem states:
where is the autocorrelation function.
White noise has constant power spectral density for all frequencies, implying:
i.e., the signal is uncorrelated at different times. Physical white noise is band-limited (e.g., Johnson noise in resistors has spectral density up to cutoff frequency ).
Green's Functions via Fourier Methods
The wave equation with retarded boundary conditions (causality) has Fourier transform:
giving:
The prescription ensures the retarded solution. Inverting the Fourier transform:
This shows that disturbances propagate on the light cone .
For the diffusion equation in with :
Inverting (a Gaussian integral):
This describes how an initial point concentration spreads out with characteristic radius , fundamental to Brownian motion and heat conduction.
Fourier Optics
Light passing through an aperture 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:
For a circular aperture of radius , this gives the Airy pattern:
where is the Bessel function. The first dark ring occurs at , defining the Rayleigh criterion for optical resolution.
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.