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<p>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.</p>

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Intelligent surrogate modeling for penetration prediction: solving forward and inverse problems with multi-fidelity data

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Danning Jing,
  • Xuguang Chen,
  • Shuo Wang,
  • Qinglin Wang,
  • Jie Liu,
  • Xinhai Chen

摘要

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.