  We develop a computationally efficient and robust algorithm for generating
pseudo-random samples from a broad class of smooth probability distributions in
one and two dimensions. The algorithm is based on inverse transform sampling
with a polynomial approximation scheme using Chebyshev polynomials, Chebyshev
grids, and low rank function approximation. Numerical experiments demonstrate
that our algorithm outperforms existing approaches.
