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08/08/2023
Contour Location for Reliability in Airfoil Simulation Experiments using Deep Gaussian Processes
Bayesian deep Gaussian processes (DGPs) outperform ordinary GPs as surro...
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05/30/2023
Non-stationary Gaussian Process Surrogates
We provide a survey of non-stationary surrogate models which utilize Gau...
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02/16/2023
Robust expected improvement for Bayesian optimization
Bayesian Optimization (BO) links Gaussian Process (GP) surrogates with s...
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01/24/2023
Active Learning for Simulator Calibration
The Kennedy and O'Hagan (KOH) calibration framework uses coupled Gaussia...
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07/20/2022
Machine learning and geospatial methods for large-scale mining data
The canonical technique for nonlinear modeling of spatial and other poin...
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04/06/2022
Vecchia-approximated Deep Gaussian Processes for Computer Experiments
Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional c...
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12/14/2021
Triangulation candidates for Bayesian optimization
Bayesian optimization is a form of sequential design: idealize input-out...
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09/11/2021
Large-scale local surrogate modeling of stochastic simulation experiments
Gaussian process (GP) regression in large-data contexts, which often ari...
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05/24/2021
Entropy-based adaptive design for contour finding and estimating reliability
In reliability analysis, methods used to estimate failure probability ar...
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01/31/2021
Multi-output calibration of a honeycomb seal via on-site surrogates
We consider large-scale industrial computer model calibration, combining...
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01/15/2021
Sensitivity Prewarping for Local Surrogate Modeling
In the continual effort to improve product quality and decrease operatio...
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12/15/2020
Active Learning for Deep Gaussian Process Surrogates
Deep Gaussian processes (DGPs) are increasingly popular as predictive mo...
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10/13/2020
Batch-sequential design and heteroskedastic surrogate modeling for delta smelt conservation
Delta smelt is an endangered fish species in the San Francisco estuary t...
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08/28/2020
Locally induced Gaussian processes for large-scale simulation experiments
Gaussian processes (GPs) serve as flexible surrogates for complex surfac...
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05/27/2020
Precision Aggregated Local Models
Large scale Gaussian process (GP) regression is infeasible for larger da...
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02/04/2020
Stochastic Simulators: An Overview with Opportunities
In modern science, deterministic computer models are often used to under...
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12/06/2018
Distance-distributed design for Gaussian process surrogates
A common challenge in computer experiments and related fields is to effi...
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10/03/2018
On-site surrogates for large-scale calibration
Motivated by a challenging computer model calibration problem from the o...
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06/13/2018
Synthesizing simulation and field data of solar irradiance
Predicting the intensity and amount of sunlight as a function of locatio...
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12/01/2017
Emulating satellite drag from large simulation experiments
Obtaining accurate estimates of satellite drag coefficients in low Earth...
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10/09/2017
Replication or exploration? Sequential design for stochastic simulation experiments
In this paper we investigate the merits of replication, and provide meth...
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05/31/2016
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
An augmented Lagrangian (AL) can convert a constrained optimization prob...
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08/09/2014
Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicin...
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09/16/2013
Sequential Design for Optimal Stopping Problems
We propose a new approach to solve optimal stopping problems via simulat...
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01/26/2012
Dynamic trees for streaming and massive data contexts
Data collection at a massive scale is becoming ubiquitous in a wide vari...
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04/29/2008
