Simulation of multi-dimensional non-gaussian non-stationary random fields

This paper presents a method for simulating multi-dimensional stochastic processes. The target process is specified by its marginal density function which can vary along the indexing set, and by its two point correlation function, which need not be stationary. The polynomial chaos expansion is used to match the marginal densities while the Karhunen–Loève representation is used to fine tune the match of the correlation function. The resulting representation of the process is in the form of a polynomial chaos expansion, which can be readily realized.