TY - GEN
T1 - Bezier Curve-Based Shape Knowledge Acquisition and Fusion for Surrogate Model Construction
AU - An, Peng
AU - Ye, Wenbin
AU - Wang, Zizhao
AU - Xiao, Hua
AU - Long, Yongsong
AU - Hao, Jia
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Surrogate model technology is a key technology in the field of engineering design with limited data. Fusion of engineering knowledge into surrogate models is an effective method to improve the prediction accuracy. However, engineering knowledge in this field describes the complex relationship between variables, which makes it difficult to obtain quantitative knowledge. Therefore, the engineering knowledge acquisition and fusion technology based on Bezier Curve for complex equipment design was proposed, which covered the entire process from knowledge acquisition to filtering and fusion. Finally, through the verification of the Unmanned Vehicle Truss design case and test functions, the experimental results show that the technology can achieve the effective acquisition of complex curve knowledge and represent multi-knowledge information effectively.
AB - Surrogate model technology is a key technology in the field of engineering design with limited data. Fusion of engineering knowledge into surrogate models is an effective method to improve the prediction accuracy. However, engineering knowledge in this field describes the complex relationship between variables, which makes it difficult to obtain quantitative knowledge. Therefore, the engineering knowledge acquisition and fusion technology based on Bezier Curve for complex equipment design was proposed, which covered the entire process from knowledge acquisition to filtering and fusion. Finally, through the verification of the Unmanned Vehicle Truss design case and test functions, the experimental results show that the technology can achieve the effective acquisition of complex curve knowledge and represent multi-knowledge information effectively.
KW - Complex equipment
KW - Engineering knowledge
KW - Knowledge representation
KW - Surrogate models
UR - http://www.scopus.com/inward/record.url?scp=85141718674&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-18461-1_22
DO - 10.1007/978-3-031-18461-1_22
M3 - Conference contribution
AN - SCOPUS:85141718674
SN - 9783031184604
T3 - Lecture Notes in Networks and Systems
SP - 328
EP - 342
BT - Proceedings of the Future Technologies Conference, FTC 2022, Volume 1
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th Future Technologies Conference, FTC 2022
Y2 - 20 October 2022 through 21 October 2022
ER -