Analysis of quasi-optimal polynomial approximations for parameterized PDEs with deterministic and stochastic coefficients

Abstract

In this work, we present a generalized methodology for analyzing the convergence of quasi-optimal Taylor and Legendre approximations, applicable to a wide class of parameterized elliptic PDEs with finite-dimensional deterministic and stochastic inputs. Such methods construct an optimal index set that corresponds to the sharp estimates of the polynomial coefficients. Our analysis furthermore represents a new approach for estimating best $M$-term approximation errors by means of coefficient bounds, without using Stechkin inequality. The framework we propose for analyzing asymptotic truncation errors is based on an extension of the underlying multi-index set into a continuous domain, and then an approximation of the cardinality (number of integer multi-indices) by its Lebesgue measure. Several types of isotropic and anisotropic (weighted) multi-index sets are explored, and rigorous proofs reveal sharp asymptotic error estimates in which we achieve sub-exponential convergence rates of the form $M \exp(-(κM)^{1/N})$, with $κ$ a constant depending on the shape and size of multi-index sets) with respect to the total number of degrees of freedom. Through several theoretical examples, we explicitly derive the constant $κ$ and use the resulting sharp bounds to illustrate the effectiveness of Legendre over Taylor approximations, as well as compare our rates of convergence with current published results. Computational evidence complements the theory and shows the advantage of our generalized framework compared to previously developed estimates.