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[SÁCH][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 …
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 …
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 …
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 …