Implementing Data-Driven Approach for Modelling Ultrasonic Wave Propagation Using Spatio-Temporal Deep Learning (SDL)

Abstract

In this paper, we proposed a data-driven spatio-temporal deep learning (SDL) model, to simulate forward and reflected ultrasonic wave propagation in the 2D geometrical domain, by implementing the convolutional long short-term memory (ConvLSTM) algorithm. The SDL model learns underlying wave physics from the spatio-temporal datasets. Two different SDL models are trained, with the following time-domain finite element (FE) simulation datasets, by applying: (1) multi-point excitation sources inside the domain and (2) single-point excitation sources on the edge of the different geometrical domains. The proposed SDL models simulate ultrasonic wave dynamics, for the forward ultrasonic wave propagation in the different geometrical domains and reflected wave propagation phenomenon, from the geometrical boundaries such as curved, T-shaped, triangular, and rectangular domains, with varying frequencies and cycles. The SDL is a reliable model, which generates simulations faster than the conventional finite element solvers.

Keywords

• data-driven modeling
• spatio-temporal datasets
• ultrasonic wave propagation
• deep learning
• RNN
• ConvLSTM