4 papers · 1 filter
Extension of graph-accelerated non-intrusive polynomial chaos to high-dimensional uncertainty quantification through the active subspace method
Bingran Wang, Nicholas C. Orndorff, Mark Sperry +1
The recently introduced graph-accelerated non-intrusive polynomial chaos (NIPC) method has shown effectiveness in solving a broad range of uncertainty quantification (UQ) problems…
A gradient-enhanced univariate dimension reduction method for uncertainty propagation
Bingran Wang, Nicholas C. Orndorff, Mark Sperry +1
The univariate dimension reduction (UDR) method stands as a way to estimate the statistical moments of the output that is effective in a large class of uncertainty quantification (…
Graph-accelerated non-intrusive polynomial chaos expansion using partially tensor-structured quadrature rules for uncertainty quantification
Bingran Wang, Nicholas C. Orndorff, John T. Hwang
Recently, the graph-accelerated non-intrusive polynomial chaos (NIPC) method has been proposed for solving uncertainty quantification (UQ) problems. This method leverages the full-…
Accelerating model evaluations in uncertainty propagation on tensor grids using computational graph transformations
Bingran Wang, Mark Sperry, Victor E. Gandarillas +1
Methods such as non-intrusive polynomial chaos (NIPC), and stochastic collocation are frequently used for uncertainty propagation problems. Particularly for low-dimensional problem…