Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise-Reference-Cited by-同舟云学术

Bayesian Optimization of Expected Quadratic Loss for Multiresponse Computer Experiments with Internal Noise

Published:2020-01 Issue:3 Volume:8 Page:891-925
ISSN:2166-2525
Container-title:SIAM/ASA Journal on Uncertainty Quantification
language:en
Short-container-title:SIAM/ASA J. Uncertainty Quantification

Author:

Tan Matthias H. Y.

Funder

General Research Fund of City University of Hong Kong

Research Grants Council, University Grants Committee

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,Discrete Mathematics and Combinatorics,Statistics, Probability and Uncertainty,Modelling and Simulation,Statistics and Probability

Link

https://epubs.siam.org/doi/pdf/10.1137/19M1272676

Reference35 articles.

1. Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2. Achieving Robust Design from Computer Simulations

3. Bayesian emulation of complex multi-output and dynamic computer models

4. Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer Experiments

5. The Knowledge-Gradient Policy for Correlated Normal Beliefs

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-objective optimization of robust parameter design based on Gaussian process model;2023 4th International Conference on Big Data & Artificial Intelligence & Software Engineering (ICBASE);2023-08-25

2. Nested Bayesian Optimization for Computer Experiments;IEEE/ASME Transactions on Mechatronics;2023-02

3. A novel Bayesian approach for multi-objective stochastic simulation optimization;Swarm and Evolutionary Computation;2022-12

4. Bayesian optimization for robust engineering design with noise factors;2022 International Conference on Cloud Computing, Big Data Applications and Software Engineering (CBASE);2022-09

5. An active learning Bayesian ensemble surrogate model for structural reliability analysis;Quality and Reliability Engineering International;2022-06-21