Multi-Fidelity Surrogate Modeling via Hierarchical Kriging With Infinite-Width Bayesian Neural Network Correlation Function

Multi-Fidelity Surrogate Modeling via Hierarchical Kriging With Infinite-Width Bayesian Neural Network Correlation Function

Hierarchical Kriging (HK) is a promising surrogate model to fuse multi-fidelity data. In theory, HK can serve as predictor for problems with any number of input dimensions. In practice, for a problem with more than 1000 variables, it is often not affordable to build such HK model due to the high dem...

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Bibliographic Details
Main Authors: Youwei He, Jiangshuo Cui
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Surrogate model
multi-fidelity
hierarchical kriging
1000-dimensional problem
Online Access:https://ieeexplore.ieee.org/document/10960298/
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