A two-step scaled physics-informed neural network for non-destructive testing of hull rib damage

Xiaoqi Chen, Yongzhen Wang, Qinglei Zeng*, Xianben Ren*, Ying Li

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

摘要

Nondestructive testing (NDT), a critical method for assessing the safety of ship structures, often requires large amounts of data. The physics-informed neural network (PINN), by integrating data with physical laws, offers an efficient approach to NDT, even when sampling data is limited. However, training PINNs often encounters challenges due to a mismatch between the gradients of the data loss and physical loss, a phenomenon known as gradient ill-conditioning. In this work, we propose a two step-scaled physics-informed neural network (TSS-PINN) to mitigate this issue, where both the variables within the neural network and those in the physical equations are scaled. Using one-dimensional hull ribs for illustration, the performance of TSS-PINN is compared with that of traditional PINN. While both methods effectively predict the distribution of elastic modulus along an ideal beam, only TSS-PINN can predict the elastic modulus distribution in hull ribs. Based on gradient analysis, we demonstrate that gradient pathology in PINNs varies with the positions. TSS-PINN can project boundary measurement points to central positions, thereby alleviating gradient ill-conditioning at the boundaries. Our findings indicate that TSS-PINN provides a simple and efficient approach to address unreasonable gradients in PINNs, especially in inverse problems such as NDT.

源语言英语
文章编号120260
期刊Ocean Engineering
319
DOI
出版状态已出版 - 1 3月 2025

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Chen, X., Wang, Y., Zeng, Q., Ren, X., & Li, Y. (2025). A two-step scaled physics-informed neural network for non-destructive testing of hull rib damage. Ocean Engineering, 319, 文章 120260. https://doi.org/10.1016/j.oceaneng.2024.120260