期刊
ULTRASONICS
卷 128, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ultras.2022.106863
关键词
Ultrasonic wave propagation; Deep learning; Convolutional LSTM; Finite element; Error propagation
This paper proposes the study of optimizing the hyperparameters of the DPAI model to simulate ultrasonic wave propagation at extended depth with lower error. The effectiveness of the trained DPAI models with varying hyperparameters is demonstrated in reducing the compounding error.
In this paper, we propose the study of optimizing the hyperparameters of deep learning Data-driven simulation -assisted-Physics learned AI (DPAI) model to simulate the ultrasonic wave propagation for extended depth with a lower error. DPAI model has layers of encoder-decoder structure with modified convolutional long short-term memory (ConvLSTM). DPAI model is trained using the finite element (FE) simulations dataset of distributed single-point to multi-point excitation sources in the 2D domain. The DPAI is the data-driven approach to apprehending the underlying physics of elastodynamic wave propagation. Six different combinations of hyperparameters (hidden dimensions, kernel size, batch size) are used in the DAPI model to study parameter optimization for lowering compounding error. The effectiveness of the trained DPAI models with varying hyperparameters is demonstrated to reduce the compounding error for modeling the deeper simulations of the single-point excitation and multi-point excitation sources. The maximum MAE on amplitude is 5.0 x 10-2, and MAPE is 2.64% on time of flight (TOF) between DPAI and FE simulations.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据