A global–local attention network for uncertainty analysis of ground penetrating radar modeling. (June 2023)

• Record Type: Journal Article
• Authors: Zhao, Yunjie; Cheng, Xi; Zhang, Taihong; Wang, Lei; Shao, Wei; Wiart, Joe
• Is Part Of: Reliability engineering & system safety. Volume 234(2023)
• Publication Date: 2023-06
• Subjects: Attention mechanism -- Deep learning -- Ground penetrating radar -- Uncertainty analysis -- Multi-scale; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2023.109176
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: A global–local attention-based feature reconstruction (GLAFR) surrogate model is proposed for uncertainty analysis (UA) in ground penetrating radar (GPR) simulation. The uncertain inputs are converted to electric fields by the surrogate model instead of the full-wave simulation, and the uncertainty of output is quantified effectively. In the model, the global feature scaling (GFS) and the local feature reconstruction (LFR) are employed to obtain the long-term and short-term relationships of features. In addition, a new loss function is proposed to accelerate the convergence of the model for training data with a wider range of input disturbances. The validity of the surrogate model is verified by the UA result from the Monte Carlo method (MCM). Compared with existing deep learning methods, the proposed approach can efficiently get higher quality predictions. Meanwhile, the Sobol indices evaluated by GLAFR are in agreement with those of MCM which requires running the full-wave simulation one thousand times to converge. Highlights: Proposing a global–local attention-based feature reconstruction (GLAFR) surrogate model. Proposing a loss function applicable to uncertainty analysis. Simulation experiments demonstrate the performance of the proposed method. GLAFR obtains similar Sobol sensitivity indices to the traditional Monte Carlo method.