6 citations · 8 across the 9 of their papers we have counts for
5 papers · 1 filter
Gradient-enhancement and Gradient Predictions for Deep Gaussian Process Modeling of Expensive Computer Experiments
Annie S. Booth
Deep Gaussian processes (DGPs) are popular surrogate models for complex nonstationary computer experiments. DGPs use one or more latent Gaussian processes (GPs) to warp the input s…
Actively Learning Joint Contours of Multiple Computer Experiments
Shih-Ni Prim, Kevin R. Quinlan, Paul Hawkins +2
Contour location---the process of sequentially training a surrogate model to identify the design inputs that result in a pre-specified response value from a single computer experim…
Revisiting Penalized Likelihood Estimation for Deterministic Computer Experiments
Ayumi Mutoh, Annie S. Booth, Jonathan W. Stallrich
Gaussian processes (GPs) are popular as nonlinear regression models for expensive computer simulations, yet GP performance relies heavily on estimation of unknown covariance parame…
Two-stage Design for Failure Probability Estimation with Gaussian Process Surrogates
Annie S. Booth, S. Ashwin Renganathan
We tackle the problem of quantifying failure probabilities for expensive deterministic computer experiments with stochastic inputs under a fixed budget. The computational cost of t…
Nonstationary Gaussian Process Surrogates
Annie S. Booth, Andrew Cooper, Robert B. Gramacy
We provide a survey of nonstationary surrogate models which utilize Gaussian processes (GPs) or variations thereof, including nonstationary kernel adaptations, partition and local…