Applied Mathematics and Mechanics >
Intelligent surrogate modeling for penetration prediction: solving forward and inverse problems with multi-fidelity data
Received date: 2025-12-16
Revised date: 2026-04-16
Online published: 2026-06-18
Supported by
Project supported by the National Natural Science Foundation of China (No.?12402349), the Natural Science Foundation of Hunan Province of China (No.?2024JJ6468), the Innovation Reserch Foundation of National University of Defense Technology of China (No.?ZK2023-11), and the National Key Research and Development Program of China (No.?2021YFB0300101)
Copyright
The analysis of penetration mechanics is critical for the offensive targeting and defensive design of underground facilities. Although computational methods are fundamental to penetration analysis, they are often constrained by a trade-off between accuracy and computational efficiency. Emerging artificial intelligence (AI) methods, with inherent strengths in modeling complex high-dimensional relationships from available data, provide promising alternatives for building intelligent surrogate models. This study proposes a fusion-enhanced radial basis function network (FE-RBFN) for penetration prediction, solving forward and inverse problems with multi-fidelity data. FE-RBFN employs three interconnected subnetworks to extract features and capture nonlinear correlations at varying fidelity levels. To overcome the challenge of data scarcity, FE-RBFN embeds a data fusion strategy to fully leverage multi-fidelity data from multiple sources. The experimental results demonstrate that our network yields rapid and precise predictions, outperforming traditional machine learning methods. Notably, in multi-fidelity scenarios, FE-RBFN exhibits robust prediction accuracy despite the limited availability of high-fidelity data.
Key words： penetration; artificial intelligence (AI); data fusion; multi-fidelity
Danning JING , Xuguang CHEN , Shuo WANG , Qinglin WANG , Jie LIU , Xinhai CHEN . Intelligent surrogate modeling for penetration prediction: solving forward and inverse problems with multi-fidelity data[J]. Applied Mathematics and Mechanics, 2026 , 47(6) : 1341 -1362 . DOI: 10.1007/s10483-026-3397-7
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