Jerrad Hampton
Jerrad Hampton
CIMNE
Verified email at cimne.upc.edu
Title
Cited by
Cited by
Year
A weighted ℓ1-minimization approach for sparse polynomial chaos expansions
J Peng, J Hampton, A Doostan
Journal of Computational Physics 267, 92-111, 2014
1722014
Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies
J Hampton, A Doostan
Journal of Computational Physics 280, 363-386, 2015
1592015
Coherence motivated sampling and convergence analysis of least squares polynomial chaos regression
J Hampton, A Doostan
Computer Methods in Applied Mechanics and Engineering 290, 73-97, 2015
1022015
On polynomial chaos expansion via gradient-enhanced ℓ1-minimization
J Peng, J Hampton, A Doostan
Journal of Computational Physics 310, 440-458, 2016
732016
Sparse polynomial chaos expansions via compressed sensing and D-optimal design
P Diaz, A Doostan, J Hampton
Computer Methods in Applied Mechanics and Engineering 336, 640-666, 2018
462018
Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction
J Hampton, HR Fairbanks, A Narayan, A Doostan
Journal of Computational Physics 368, 315-332, 2018
412018
Basis adaptive sample efficient polynomial chaos (BASE-PC)
J Hampton, A Doostan
Journal of Computational Physics 371, 20-49, 2018
212018
Compressive sampling methods for sparse polynomial chaos expansions
J Hampton, A Doostan
Handbook of uncertainty quantification, 1-29, 2016
192016
Topology optimization under uncertainty using a stochastic gradient-based approach
S De, J Hampton, K Maute, A Doostan
Structural and Multidisciplinary Optimization 62 (5), 2255-2278, 2020
142020
Parametric/stochastic model reduction: Low-rank representation, non-intrusive bi-fidelity approximation, and convergence analysis
J Hampton, H Fairbanks, A Narayan, A Doostan
arXiv preprint arXiv:1709.03661, 2017
92017
Reduced cost mission design using surrogate models
JD Feldhacker, BA Jones, A Doostan, J Hampton
Advances in Space Research 57 (2), 588-603, 2016
92016
Embedded multilevel monte carlo for uncertainty quantification in random domains
S Badia, J Hampton, J Principe
International Journal for Uncertainty Quantification 11 (1), 2021
62021
Estimation of distribution overlap of urn models
J Hampton, ME Lladser
PloS one 7 (11), e42368, 2012
52012
Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification
F Newberry, J Hampton, K Jansen, A Doostan
arXiv preprint arXiv:2104.07462, 2021
2021
Correction: Estimation of Distribution Overlap of Urn Models
J Hampton, ME Lladser
Plos one 9 (1), 2014
2014
Dissimilarity and Optimal Sampling in Urn Ensembles
J Hampton
University of Colorado at Boulder, 2012
2012
The Shifted Boundary Method: A new approach to embedded domain computations.
G Scovazzi, A Main, T Song, N Atallah, O Colomés, L Nouveau, ...
Embedded multilevel Monte Carlo for uncertainty quantification in complex random domains
S Badia, J Hampton, J Principe
Reduced Cost Maneuver Design Using Surrogate Models
JD Feldhacker, BA Jones, A Doostan, J Hampton
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Articles 1–19