A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
[图书][B] Uncertainty quantification: theory, implementation, and applications
RC Smith - 2024 - SIAM
Uncertainty quantification serves a central role for simulation-based analysis of physical,
engineering, and biological applications using mechanistic models. From a broad …
engineering, and biological applications using mechanistic models. From a broad …
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 …
A review on computer model calibration
Abstract Model calibration is crucial for optimizing the performance of complex computer
models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological …
models across various disciplines. In the era of Industry 4.0, symbolizing rapid technological …
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 …
Bayesian active learning with fully Bayesian Gaussian processes
The bias-variance trade-off is a well-known problem in machine learning that only gets more
pronounced the less available data there is. In active learning, where labeled data is scarce …
pronounced the less available data there is. In active learning, where labeled data is scarce …
Triangulation candidates for bayesian optimization
Bayesian optimization involves" inner optimization" over a new-data acquisition criterion
which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart …
which is non-convex/highly multi-modal, may be non-differentiable, or may otherwise thwart …
Deep Gaussian process emulation using stochastic imputation
D Ming, D Williamson, S Guillas - Technometrics, 2023 - Taylor & Francis
Abstract Deep Gaussian processes (DGPs) provide a rich class of models that can better
represent functions with varying regimes or sharp changes, compared to conventional GPs …
represent functions with varying regimes or sharp changes, compared to conventional GPs …
[HTML][HTML] Reliable emulation of complex functionals by active learning with error control
A statistical emulator can be used as a surrogate of complex physics-based calculations to
drastically reduce the computational cost. Its successful implementation hinges on an …
drastically reduce the computational cost. Its successful implementation hinges on an …
Gaussian process autoregression models for the evolution of polycrystalline microstructures subjected to arbitrary stretching tensors
S Hashemi, SR Kalidindi - International Journal of Plasticity, 2023 - Elsevier
Crystal plasticity finite element models (CPFEM) have shown tremendous potential for
simulating the microstructure evolution paths in polycrystalline aggregates subjected to …
simulating the microstructure evolution paths in polycrystalline aggregates subjected to …