Multi-fidelity meta modeling using composite neural network with online adaptive basis technique
- Title
- Multi-fidelity meta modeling using composite neural network with online adaptive basis technique
- Author
- 양현익
- Keywords
- Machine learning; Online adaptive basis method; Composite neural network; Multi-fidelity approach; Reduced order modeling; Data-driven modeling
- Issue Date
- 2021-10
- Publisher
- ELSEVIER SCIENCE SA
- Citation
- COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, v. 388, NO 1, Page. 1-34
- Abstract
- A composite neural network (NN) is a way to improve the reliability of a prediction field by implementing a multi-fidelity
model when high-fidelity data are extremely limited. The reliability of the composite NN depends highly on the quantity and
quality of low-fidelity data. Satisfying both issues simultaneously is difficult in engineering practices even if low-fidelity data are
considered. With this motivation, we suggest a strategy for ameliorating low-fidelity data to efficiently improve the prediction
fields of a composite NN. In the proposed strategy, a reduced-order modeling (ROM) is used to supply a large number of
low-fidelity data with relatively low computational costs. To decrease ROM errors that may decisively hinder the training of a
cross-correlation between low-fidelity and high-fidelity data, the low-fidelity data are updated using an efficient adaptive basis
technique. The adaptive basis in this work is calculated by optimizing the local error data to avoid huge computational costs.
As a result, the low-fidelity data can better reflect the trend while satisfying the condition in which the low-fidelity data should
be sufficiently provided through an efficient procedure. Furthermore, the strategy applied in this study is conducted without
any modifications of the given high-fidelity data. Hence, even if high-fidelity data may no longer be obtained, it is possible
to efficiently obtain the high-quality prediction field of the composite NN. A detailed strategy is proposed herein, and its
performance is evaluated through various numerical examples.
c⃝ 2021 Elsevier B.V. All rights reserved.
- URI
- https://www.sciencedirect.com/science/article/pii/S0045782521005715https://repository.hanyang.ac.kr/handle/20.500.11754/170251
- ISSN
- 0045-7825
- DOI
- 10.1016/j.cma.2021.114258
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MECHANICAL ENGINEERING(기계공학과) > Articles
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