  In surrogate modeling, polynomial chaos expansion (PCE) is popularly utilized
to represent the random model responses, which are computationally expensive
and usually obtained by deterministic numerical modeling approaches including
finite element and finite-difference time-domain methods. Recently, efforts
have been made on improving the prediction performance of the PCE-based model
and building efficiency by only selecting the influential basis polynomials
(e.g., via the approach of least angle regression). This paper proposes an
approach, named as resampled PCE (rPCE), to further optimize the selection by
making use of the knowledge that the true model is fixed despite the
statistical uncertainty inherent to sampling in the training. By simulating
data variation via resampling ($k$-fold division utilized here) and collecting
the selected polynomials with respect to all resamples, polynomials are ranked
mainly according to the selection frequency. The resampling scheme (the value
of $k$ here) matters much and various configurations are considered and
compared. The proposed resampled PCE is implemented with two popular selection
techniques, namely least angle regression and orthogonal matching pursuit, and
a combination thereof. The performance of the proposed algorithm is
demonstrated on two analytical examples, a benchmark problem in structural
mechanics, as well as a realistic case study in computational dosimetry.
