PCE method was first conceived by Wiener {Wiener, 1938} as a means to integrate differential equation-type operators where differential Brownian motion, at the time viewed as chaotic, was an external forcing influence.
Polynomial Chaos Expansion is a generalized Fourier expansion on the \(L^2(\Omega, \Sigma, P)\) space of random variables, distinguished by the stochastic bases used, to approximate a random variable.
PCE-based approximations have the form: \[ X^{(n)} = \sum_{i=0}^{n} g_i \Gamma_i (\xi) \]
(Strictly speaking, it only applies to random variables that are transformations of that standard Gaussian random variable.)
So we are essentially approximating distributions and assuming the transformation from a base RV (e.g. standard normal) to an arbitrarily distributed RV exists.
Note:
Strengths of PCE:
The Hermite polynomials can be defined as:
\[ \Gamma_n(x)=(-1)^n e^{\frac{x^2}{2}}\tfrac{\mathrm{d}^n }{\mathrm{d}x^n} e^{-\frac{x^2}{2}} = \bigg (x-\frac{\mathrm{d}}{\mathrm{d}x} \bigg )^n\cdot 1, \quad n \in \mathbb{N} \]
The first four polynomials are \( 1, x, x^2-1, x^3 - 3x \).
Hermite polynomials is the set of complete orthogonal polynomials when the weighting function of the inner product on a Hilbert space is standard Gaussian PDF.