J
Jerrad Hampton
Researcher at University of Colorado Boulder
Publications - 20
Citations - 938
Jerrad Hampton is an academic researcher from University of Colorado Boulder. The author has contributed to research in topics: Uncertainty quantification & Polynomial chaos. The author has an hindex of 10, co-authored 20 publications receiving 734 citations.
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Compressive sampling of polynomial chaos expansions
Jerrad Hampton,Alireza Doostan +1 more
TL;DR: The coherence-optimal sampling scheme is proposed: a Markov Chain Monte Carlo sampling, which directly uses the basis functions under consideration to achieve a statistical optimality among all sampling schemes with identical support.
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A weighted l 1 -minimization approach for sparse polynomial chaos expansions.
TL;DR: This work modify the standard l1l1-minimization algorithm, originally proposed in the context of compressive sampling, using a priori information about the decay of the PC coefficients, when available, and refers to the resulting algorithm as weighted l1l 1- Minimization.
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Coherence motivated sampling and convergence analysis of least squares polynomial Chaos regression
Jerrad Hampton,Alireza Doostan +1 more
TL;DR: In this paper, a Markov Chain Monte Carlo (MCMC) sampling strategy was proposed for orthogonal polynomials of Hermite and Legendre types under each respective natural sampling distribution.
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On polynomial chaos expansion via gradient-enhanced ℓ 1 -minimization
TL;DR: This work investigates a gradient-enhanced ?
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Sparse polynomial chaos expansions via compressed sensing and D-optimal design
TL;DR: In this article, the authors proposed a greedy algorithm for sparse polynomial chaos (SPC) approximation, which is based on the theory of optimal design of experiments (ODE) and incorporates topics from ODE to estimate the PC coefficients.