
Largescale local surrogate modeling of stochastic simulation experiments
Gaussian process (GP) regression in largedata contexts, which often ari...
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Entropybased adaptive design for contour finding and estimating reliability
In reliability analysis, methods used to estimate failure probability ar...
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Multioutput calibration of a honeycomb seal via onsite surrogates
We consider largescale industrial computer model calibration, combining...
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Sensitivity Prewarping for Local Surrogate Modeling
In the continual effort to improve product quality and decrease operatio...
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Active Learning for Deep Gaussian Process Surrogates
Deep Gaussian processes (DGPs) are increasingly popular as predictive mo...
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Batchsequential 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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Locally induced Gaussian processes for largescale simulation experiments
Gaussian processes (GPs) serve as flexible surrogates for complex surfac...
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Precision Aggregated Local Models
Large scale Gaussian process (GP) regression is infeasible for larger da...
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Stochastic Simulators: An Overview with Opportunities
In modern science, deterministic computer models are often used to under...
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Distancedistributed design for Gaussian process surrogates
A common challenge in computer experiments and related fields is to effi...
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Onsite surrogates for largescale calibration
Motivated by a challenging computer model calibration problem from the o...
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Synthesizing simulation and field data of solar irradiance
Predicting the intensity and amount of sunlight as a function of locatio...
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Emulating satellite drag from large simulation experiments
Obtaining accurate estimates of satellite drag coefficients in low Earth...
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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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Bayesian optimization under mixed constraints with a slackvariable augmented Lagrangian
An augmented Lagrangian (AL) can convert a constrained optimization prob...
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Gaussian Process Structural Equation Models with Latent Variables
In a variety of disciplines such as social sciences, psychology, medicin...
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Sequential Design for Optimal Stopping Problems
We propose a new approach to solve optimal stopping problems via simulat...
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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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Gaussian Processes and Limiting Linear Models
Gaussian processes retain the linear model either as a special case, or ...
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Robert B. Gramacy
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