Adaptive Sparse Polynomial Chaos Expansions via Leja Interpolation

This work suggests an interpolation-based stochastic collocation method for the non-intrusive and adaptive construction of sparse polynomial chaos expansions (PCEs). Unlike pseudo-spectral projection and regression-based stochastic collocation methods, the proposed approach results in PCEs featuring one polynomial term per collocation point. Moreover, the resulting PCEs are interpolating, i.e., they are exact on the interpolation nodes/collocation points. Once available, an interpolating PCE can be used as an inexpensive surrogate model, or be post-processed for the purposes of uncertainty quantification and sensitivity analysis. The main idea is conceptually simple and relies on the use of Leja sequence points as interpolation nodes. Using Newton-like, hierarchical basis polynomials defined upon Leja sequences, a sparse-grid interpolation can be derived, the basis polynomials of which are unique in terms of their multivariate degrees. A dimension-adaptive scheme can be employed for the construction of an anisotropic interpolation. Due to the degree uniqueness, a one-to-one transform to orthogonal polynomials of the exact same degrees is possible and shall result in an interpolating PCE. However, since each Leja node defines a unique Newton basis polynomial, an implicit one-to-one map between Leja nodes and orthogonal basis polynomials exists as well. Therefore, the in-between steps of hierarchical interpolation and basis transform can be discarded altogether, and the interpolating PCE can be computed directly. For directly computed, adaptive, anisotropic interpolating PCEs, the dimension-adaptive algorithm is modified accordingly. A series of numerical experiments verify the suggested approach in both low and moderately high-dimensional settings, as well as for various input distributions.