A weighted ℓ1-minimization approach for sparse polynomial chaos expansions J Peng, J Hampton, A Doostan Journal of Computational Physics 267, 92-111, 2014 | 172 | 2014 |

Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies J Hampton, A Doostan Journal of Computational Physics 280, 363-386, 2015 | 159 | 2015 |

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 | 102 | 2015 |

On polynomial chaos expansion via gradient-enhanced ℓ1-minimization J Peng, J Hampton, A Doostan Journal of Computational Physics 310, 440-458, 2016 | 73 | 2016 |

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 | 46 | 2018 |

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 | 41 | 2018 |

Basis adaptive sample efficient polynomial chaos (BASE-PC) J Hampton, A Doostan Journal of Computational Physics 371, 20-49, 2018 | 21 | 2018 |

Compressive sampling methods for sparse polynomial chaos expansions J Hampton, A Doostan Handbook of uncertainty quantification, 1-29, 2016 | 19 | 2016 |

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 | 14 | 2020 |

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 | 9 | 2017 |

Reduced cost mission design using surrogate models JD Feldhacker, BA Jones, A Doostan, J Hampton Advances in Space Research 57 (2), 588-603, 2016 | 9 | 2016 |

Embedded multilevel monte carlo for uncertainty quantification in random domains S Badia, J Hampton, J Principe International Journal for Uncertainty Quantification 11 (1), 2021 | 6 | 2021 |

Estimation of distribution overlap of urn models J Hampton, ME Lladser PloS one 7 (11), e42368, 2012 | 5 | 2012 |

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 | | |