224 citations · 802 across the 12 of their papers we have counts for
3 papers · 1 filter
Sparse Polynomial Chaos Expansions via Compressed Sensing and D-optimal Design
Paul Diaz, Alireza Doostan, Jerrad Hampton
In the field of uncertainty quantification, sparse polynomial chaos (PC) expansions are commonly used by researchers for a variety of purposes, such as surrogate modeling. Ideas fr…
Parametric/Stochastic Model Reduction: Low-Rank Representation, Non-Intrusive Bi-Fidelity Approximation, and Convergence Analysis
Jerrad Hampton, Hillary Fairbanks, Akil Narayan +1
For practical model-based demands, such as design space exploration and uncertainty quantification (UQ), a high-fidelity model that produces accurate outputs often has high computa…
Basis Adaptive Sample Efficient Polynomial Chaos (BASE-PC)
Jerrad Hampton, Alireza Doostan
For a large class of orthogonal basis functions, there has been a recent identification of expansion methods for computing accurate, stable approximations of a quantity of interest…