A weighted l1-minimization approach for sparse polynomial chaos expansions. - Ji Peng; Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2014)

Compressive sampling of polynomial chaos expansions: Convergence analysis and sampling strategies. - Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2015)

On polynomial chaos expansion via gradient-enhanced ℓ1-minimization. - Ji Peng; Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2016)

Basis adaptive sample efficient polynomial chaos (BASE-PC). - Jerrad Hampton; Alireza Doostan (J. Comput. Phys., 2018)

Practical error bounds for a non-intrusive bi-fidelity approach to parametric/stochastic model reduction. - Jerrad Hampton; Hillary R. Fairbanks; Akil Narayan 001; Alireza Doostan (J. Comput. Phys., 2018)

Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach. - Subhayan De; Jerrad Hampton; Kurt Maute; Alireza Doostan (CoRR, 2019)

Embedded multilevel Monte Carlo for uncertainty quantification in random domains. - Santiago Badia; Jerrad Hampton; Javier Principe (CoRR, 2019)

Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification. - Felix Newberry; Jerrad Hampton; Kenneth E. Jansen; Alireza Doostan (CoRR, 2021)

A massively parallel implementation of multilevel Monte Carlo for finite element models. - Santiago Badia; Jerrad Hampton; Javier Principe (Math. Comput. Simul., 2023)

Improving Voltage Ride-Through Procedures in Distributed Generation Systems by Reinforcement Learning. - Antonio Camacho; Manel Velasco; Pau Martí; Cecilio Angulo; Jerrad Hampton (CCIA, 2024)