Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method
Multi-fidelity modeling (MFM) frameworks, especially the Bayesian MFM, have gained popularity in simulation based modeling, uncertainty quantification and optimization, due to the potential for reducing computational budget. In the view of multi-output modeling, the MFM approximates the high-/low-fi...
Saved in:
Main Authors: | Liu, Haitao, Ong, Yew-Soon, Cai, Jianfei, Wang, Yi |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/139701 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
When Gaussian process meets big data : a review of scalable GPs
by: Liu, Haitao, et al.
Published: (2021) -
Remarks on multi-output Gaussian process regression
by: Liu, Haitao, et al.
Published: (2020) -
Understanding and comparing scalable Gaussian process regression for big data
by: Liu, Haitao, et al.
Published: (2020) -
MULTI-FIDELITY OPTIMIZATION WITH GAUSSIAN REGRESSION ON ORDINAL TRANSFORMATION SPACE
by: CHEN MIN
Published: (2017) -
Modulating scalable Gaussian processes for expressive statistical learning
by: Liu, Haitao, et al.
Published: (2022)