3 citations · 3 across the 4 of their papers we have counts for
4 papers
Large-scale local surrogate modeling of stochastic simulation experiments
D Austin Cole, Robert B Gramacy, Mike Ludkovski
Gaussian process (GP) surrogate modeling for large computer experiments is limited by cubic runtimes, especially with data from stochastic simulations with input-dependent noise. A…
Entropy-based adaptive design for contour finding and estimating reliability
D. Austin Cole, Robert B. Gramacy, James E. Warner +3
In reliability analysis, methods used to estimate failure probability are often limited by the costs associated with model evaluations. Many of these methods, such as multifidelity…
Locally induced Gaussian processes for large-scale simulation experiments
D. Austin Cole, Ryan Christianson, Robert B. Gramacy
Gaussian processes (GPs) serve as flexible surrogates for complex surfaces, but buckle under the cubic cost of matrix decompositions with big training data sizes. Geospatial and ma…
Distance-distributed design for Gaussian process surrogates
Boya Zhang, D. Austin Cole, Robert B. Gramacy
A common challenge in computer experiments and related fields is to efficiently explore the input space using a small number of samples, i.e., the experimental design problem. Much…