Vol. 20 No. 4 (2023): Journal of Non Destructive Testing and Evaluation (JNDE), Dec 2023
Research Papers

DPAI: In-Situ Process Intelligence using Data-Driven Simulation-Assisted-Physics Aware AI (DPAI) for Simulating Wave Dynamics

Published 07-12-2023


  • DPAI,
  • Ultrasonic Wave simulation,
  • Phased Array,
  • FE,
  • Deep Learning,
  • RNN,
  • ConvLSTM
  • ...More

How to Cite

Thulsiram Gantala, & Krishnan Balasubramaniam. (2023). DPAI: In-Situ Process Intelligence using Data-Driven Simulation-Assisted-Physics Aware AI (DPAI) for Simulating Wave Dynamics. Journal of Non-Destructive Testing and Evaluation (JNDE), 20(4), 60–69. Retrieved from https://jnde.isnt.in/index.php/JNDE/article/view/68


AI models such as convolutional long short-term memory (ConvLSTM) recurrent neural network (RNN) have been shown here to have the capability to simulate ultrasonic wave propagation in the 2-D domain. This DPAI approach uses the Data-driven but simulation-assisted-Physics aware approach to utilizing AI networks. Our DPAI model comprises ConvLSTM with an encoder-decoder structure, which learns a representation of spatio-temporal features from the input sequence datasets. The DPAI model is trained with finite element (FE) time-domain simulation datasets consisting of distributed single and multi-point source excitation in the medium, reflection from the simple boundaries, and phased array steering. Here, this approach, called the DPAI model, is demonstrated for modelling multiple point sources to simulate forward wave propagation, reflection from the boundaries, and phased array beam steering ultrasound wave dynamics in a 2D plane. The trained DPAI model was found to be significantly faster in generating simulations for the time evolution of field values in the elastodynamic problem when compared to the conventional finite element explicit dynamic solvers.


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