DPAI: A Data-driven simulation-assisted-Physics learned AI model for transient ultrasonic wave propagation

Abstract

In this paper, we propose a deep neural network model to simulate the transient ultrasonic wave propagation in the 2D domain by implementing the Data driven-simulation-assisted-Physics learned AI (DPAI) model. The DPAI model consists of modified convolutional long short-term memory (ConvLSTM) with an encoder–decoder structure, which learns the representation of spatio-temporal dependence from input sequence data. The DPAI uses the data-driven approach to understand the underlying physics of elastic wave propagation in a medium. This model is trained with simulation-assisted finite element simulation datasets consisting of distributed single and multi-point excitation sources in the medium. The effectiveness of the proposed approach is demonstrated by modeling a wide range of scenarios in elastodynamic physics, such as multiple point sources, varying excitation parameters, and wave propagation in a large 2D domain. The trained DPAI model is tested and compared against FE modeling with respect to accuracy and computational time.