514 citations · 1.3k across the 26 of their papers we have counts for
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Global sensitivity metrics from active subspaces
Paul G. Constantine, Paul Diaz
Predictions from science and engineering models depend on several input parameters. Global sensitivity analysis quantifies the importance of each input parameter, which can lead to…
Accelerating MCMC with active subspaces
Paul G. Constantine, Carson Kent, Tan Bui-Thanh
The Markov chain Monte Carlo (MCMC) method is the computational workhorse for Bayesian inverse problems. However, MCMC struggles in high-dimensional parameter spaces, since its ite…
Computing Active Subspaces Efficiently with Gradient Sketching
Paul G. Constantine, Armin Eftekhari, Michael B. Wakin
Active subspaces are an emerging set of tools for identifying and exploiting the most important directions in the space of a computer simulation's input parameters; these direction…