One of the open problems in the field of forward uncertainty quantification (UQ) is the ability to form accurate assessments of uncertainty having
only incomplete information about the distribution of random inputs. Another
challenge is to efficiently make use of limited training data for UQ predictions
of complex engineering problems, particularly with high dimensional random
parameters. We address these challenges by combining data-driven polynomial chaos expansions with a recently developed preconditioned sparse approximation approach for UQ problems. The first task in this two-step process
is to employ the procedure developed in [1] to construct an "arbitrary" polynomial chaos expansion basis using a finite number of statistical moments of the
random inputs. The second step is a novel procedure to effect sparse approximation via $ℓ$^{1} minimization in order to quantify the forward uncertainty. To
enhance the performance of the preconditioned $ℓ$^{1} minimization problem, we
sample from the so-called induced distribution, instead of using Monte Carlo (MC) sampling from the original, unknown probability measure. We demonstrate on test problems that induced sampling is a competitive and often better
choice compared with sampling from asymptotically optimal measures (such
as the equilibrium measure) when we have incomplete information about the
distribution. We demonstrate the capacity of the proposed induced sampling
algorithm via sparse representation with limited data on test functions, and on
a Kirchoff plating bending problem with random Young's modulus.