[图书][B] Surrogates: Gaussian process modeling, design, and optimization for the applied sciences
RB Gramacy - 2020 - taylorfrancis.com
Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …
Active learning for deep Gaussian process surrogates
Abstract Deep Gaussian processes (DGPs) are increasingly popular as predictive models in
machine learning for their nonstationary flexibility and ability to cope with abrupt regime …
machine learning for their nonstationary flexibility and ability to cope with abrupt regime …
Traditional kriging versus modern Gaussian processes for large‐scale mining data
RB Christianson, RM Pollyea… - Statistical Analysis and …, 2023 - Wiley Online Library
The canonical technique for nonlinear modeling of spatial/point‐referenced data is known
as kriging in geostatistics, and as Gaussian Process (GP) regression for surrogate modeling …
as kriging in geostatistics, and as Gaussian Process (GP) regression for surrogate modeling …
Vecchia-approximated deep Gaussian processes for computer experiments
Abstract Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
composition, in which intermediate GP layers warp the original inputs, providing flexibility to …
Scaled Vecchia approximation for fast computer-model emulation
Many scientific phenomena are studied using computer experiments consisting of multiple
runs of a computer model while varying the input settings. Gaussian processes (GPs) are a …
runs of a computer model while varying the input settings. Gaussian processes (GPs) are a …
Non-stationary Gaussian process surrogates
We provide a survey of non-stationary surrogate models which utilize Gaussian processes
(GPs) or variations thereof, including non-stationary kernel adaptations, partition and local …
(GPs) or variations thereof, including non-stationary kernel adaptations, partition and local …
Generative ai for bayesian computation
Bayesian Generative AI (BayesGen-AI) methods are developed and applied to Bayesian
computation. BayesGen-AI reconstructs the posterior distribution by directly modeling the …
computation. BayesGen-AI reconstructs the posterior distribution by directly modeling the …
Locally induced Gaussian processes for large-scale simulation experiments
DA Cole, RB Christianson, RB Gramacy - Statistics and Computing, 2021 - Springer
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 …
under the cubic cost of matrix decompositions with big training data sizes. Geospatial and …
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples
Data subsampling has become widely recognized as a tool to overcome computational and
economic bottlenecks in analyzing massive datasets. We contribute to the development of …
economic bottlenecks in analyzing massive datasets. We contribute to the development of …
A comparison of Gaussian processes and neural networks for computer model emulation and calibration
S Myren, E Lawrence - … Analysis and Data Mining: The ASA …, 2021 - Wiley Online Library
Abstract The Department of Energy relies on complex physics simulations for prediction in
domains like cosmology, nuclear theory, and materials science. These simulations are often …
domains like cosmology, nuclear theory, and materials science. These simulations are often …