The ultrasonic guided wave technology plays a significant role in the field of non-destructive testing as it employs acoustic waves with the advantages of high propa-gation efficiency and low energy consumption during the inspect process.However,the theoretical solutions to guided wave scattering problems with assumptions such as the Born approximation have led to the poor quality of the reconstructed results.Besides,the scattering signals collected from industry sectors are often noised and nonstationary.To address these issues,a novel physics-informed framework(PIF)for the quantitative reconstruction of defects by means of the integration of the data-driven method with the guided wave scattering analysis is proposed in this paper.Based on the geometrical information of defects and initial results obtained by the PIF-based analysis of defect reconstructions,a deep-learning neural network model is built to reveal the physical rela-tionship between the defects and the noisy detection signals.This learning model is then adopted to assess and characterize the defect profiles in structures,improve the accuracy of the analytical model,and eliminate the impact of the noise pollution in the process of inspection.To demonstrate the advantages of the developed PIF for the complex defect reconstructions with the capability of denoising,several numerical examples are carried out.The results show that the PIF has greater accuracy for the reconstruction of defects

State Key Laboratory of Mechanics and Control of Mechanical Structures,College of Aerospace Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;College of Engineering,Ocean University of China,Qingdao 266100,Shandong Province,China;School of Engineering,University of East Anglia,Norwich NR4 7TJ,U.K.

[1]Qi LI;Fushun LIU;Bin WANG;D.Z.LIU;Zhenghua QIAN-.A novel physics-informed framework for reconstruction of structural defects)[J].应用数学和力学（英文版）,2022(11):1717-1730

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