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...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/139701 |
| _version_ | 1811687564238651392 |
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| author | Liu, Haitao Ong, Yew-Soon Cai, Jianfei Wang, Yi |
| author2 | School of Computer Science and Engineering |
| author_facet | School of Computer Science and Engineering Liu, Haitao Ong, Yew-Soon Cai, Jianfei Wang, Yi |
| author_sort | Liu, Haitao |
| collection | NTU |
| description | 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-fidelity outputs simultaneously by considering the output correlations, and particularly, it transfers knowledge from the inexpensive low-fidelity outputs that have many training points to enhance the modeling of the expensive high-fidelity output that has a few training points. This article presents a novel multi-fidelity Gaussian process for modeling with diverse data structures. The diverse data structures mainly refer to the diversity of high-fidelity sample distributions, i.e., the high-fidelity points may randomly fill the domain, or more challengingly, they may cluster in some subregions. The proposed multi-fidelity model is composed of a global trend term and a local residual term. Particularly, the flexible residual term extracts both the shared and output-specific residual information via a data-driven weight parameter. Numerical experiments on two synthetic examples, an aircraft example and a stochastic incompressible flow example reveal that this very promising Bayesian MFM approach is capable of effectively extracting the low-fidelity information for facilitating the modeling of the high-fidelity output using diverse data structures. |
| first_indexed | 2024-10-01T05:18:19Z |
| format | Journal Article |
| id | ntu-10356/139701 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:18:19Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1397012020-05-21T03:37:53Z Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method Liu, Haitao Ong, Yew-Soon Cai, Jianfei Wang, Yi School of Computer Science and Engineering Rolls-Royce@NTU Corporate Lab Data Science and Artificial Intelligence Research Center Engineering::Computer science and engineering Multi-fidelity Modeling Gaussian Process Regression 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-fidelity outputs simultaneously by considering the output correlations, and particularly, it transfers knowledge from the inexpensive low-fidelity outputs that have many training points to enhance the modeling of the expensive high-fidelity output that has a few training points. This article presents a novel multi-fidelity Gaussian process for modeling with diverse data structures. The diverse data structures mainly refer to the diversity of high-fidelity sample distributions, i.e., the high-fidelity points may randomly fill the domain, or more challengingly, they may cluster in some subregions. The proposed multi-fidelity model is composed of a global trend term and a local residual term. Particularly, the flexible residual term extracts both the shared and output-specific residual information via a data-driven weight parameter. Numerical experiments on two synthetic examples, an aircraft example and a stochastic incompressible flow example reveal that this very promising Bayesian MFM approach is capable of effectively extracting the low-fidelity information for facilitating the modeling of the high-fidelity output using diverse data structures. NRF (Natl Research Foundation, S’pore) 2020-05-21T03:37:53Z 2020-05-21T03:37:53Z 2017 Journal Article Liu, H., Ong, Y.-S., Cai, J., & Wang, Y. (2018). Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method. Engineering Applications of Artificial Intelligence, 67, 211-225. doi:10.1016/j.engappai.2017.10.008 0952-1976 https://hdl.handle.net/10356/139701 10.1016/j.engappai.2017.10.008 2-s2.0-85032282674 67 211 225 en Engineering Applications of Artificial Intelligence © 2017 Elsevier Ltd. All rights reserved. |
| spellingShingle | Engineering::Computer science and engineering Multi-fidelity Modeling Gaussian Process Regression Liu, Haitao Ong, Yew-Soon Cai, Jianfei Wang, Yi Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method |
| title | Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method |
| title_full | Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method |
| title_fullStr | Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method |
| title_full_unstemmed | Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method |
| title_short | Cope with diverse data structures in multi-fidelity modeling : a Gaussian process method |
| title_sort | cope with diverse data structures in multi fidelity modeling a gaussian process method |
| topic | Engineering::Computer science and engineering Multi-fidelity Modeling Gaussian Process Regression |
| url | https://hdl.handle.net/10356/139701 |
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