[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 …

A review on computer model calibration

CL Sung, R Tuo - Wiley Interdisciplinary Reviews …, 2024 - Wiley Online Library
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 …

Sequential Bayesian experimental design for calibration of expensive simulation models

Ö Sürer, M Plumlee, SM Wild - Technometrics, 2024 - Taylor & Francis
Simulation models of critical systems often have parameters that need to be calibrated using
observed data. For expensive simulation models, calibration is done using an emulator of …

Constructing a simulation surrogate with partially observed output

MYH Chan, M Plumlee, SM Wild - Technometrics, 2024 - Taylor & Francis
Gaussian process surrogates are a popular alternative to directly using computationally
expensive simulation models. When the simulation output consists of many responses …

Active learning for simulator calibration

S Koermer, J Loda, A Noble, RB Gramacy - arXiv preprint arXiv …, 2023 - arxiv.org
The Kennedy and O'Hagan (KOH) calibration framework uses coupled Gaussian processes
(GPs) to meta-model an expensive simulator (first GP), tune its' knobs'(calibration inputs) to …

Data-driven uncertainty quantification in macroscopic traffic flow models

A Würth, M Binois, P Goatin, S Göttlich - Advances in Computational …, 2022 - Springer
We propose a Bayesian approach for parameter uncertainty quantification in macroscopic
traffic flow models from cross-sectional data. We consider both a simple first order model …

Surrogate Active Subspaces for Jump-Discontinuous Functions

N Wycoff - … Conference on Artificial Intelligence and Statistics, 2024 - proceedings.mlr.press
Surrogate modeling and active subspaces have emerged as powerful paradigms in
computational science and engineering. Porting such techniques to computational models …

Simulation experiment design for calibration via active learning

Ö Sürer - Journal of Quality Technology, 2024 - Taylor & Francis
Simulation models often have parameters as input and return outputs to understand the
behavior of complex systems. Calibration is the process of estimating the values of the …

Augmenting a simulation campaign for hybrid computer model and field data experiments

S Koermer, J Loda, A Noble, RB Gramacy - Technometrics, 2024 - Taylor & Francis
Abstract The Kennedy and O'Hagan (KOH) calibration framework uses coupled Gaussian
processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs”(calibration …

High-Dimensional Gaussian Process Methods for Uncertainty Quantification

MYH Chan - 2023 - search.proquest.com
This thesis concerns the construction and analysis of emulators for high-dimensional
computer simulation outputs. The construction of emulators is beneficial when computer …