224 citations · 802 across the 12 of their papers we have counts for
12 papers
A Massively Parallel Implementation of Multilevel Monte Carlo for Finite Element Models
Santiago Badia, Jerrad Hampton, Javier Principe
The Multilevel Monte Carlo (MLMC) method has proven to be an effective variance-reduction statistical method for Uncertainty Quantification (UQ) in Partial Differential Equation (P…
Bi-fidelity Reduced Polynomial Chaos Expansion for Uncertainty Quantification
Felix Newberry, Jerrad Hampton, Kenneth Jansen +1
A ubiquitous challenge in design space exploration or uncertainty quantification of complex engineering problems is the minimization of computational cost. A useful tool to ease th…
Embedded multilevel Monte Carlo for uncertainty quantification in random domains
Santiago Badia, Jerrad Hampton, Javier Principe
The multilevel Monte Carlo (MLMC) method has proven to be an effective variance-reduction statistical method for Uncertainty quantification in PDE models. It combines approximation…
Topology Optimization under Uncertainty using a Stochastic Gradient-based Approach
Subhayan De, Jerrad Hampton, Kurt Maute +1
Topology optimization under uncertainty (TOuU) often defines objectives and constraints by statistical moments of geometric and physical quantities of interest. Most traditional TO…
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…