Krishnan Balasubramanian

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 5.0×10-2, and MAPE is 2.64% on time of flight (TOF) between DPAI and FE simulations.

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.

For defense applications, rapid X-ray inspection of propellant samples is essential for the identification and assessment of defects. Automation of this process using artificial intelligence is possible by properly training a neural network model. Convolution Neural Networks (CNNs) have recently demonstrated excellent success in both the tasks of image recognition and localisation using an adequate amount of data. In real-world, it’s not an easy task to produce the correct amount of experimental data required for the deep neural network to operate. In this work, we propose a method for producing synthetic radiographic data that is supported by ray tracing based radiographic simulations for the deep learning algorithms to automatically detect anomaly in X-ray images. The simulation results, which are then supplemented by noise extracted from the experimental data, show a good comparison with the measurements. This Simulation assisted Automatic Defect Recognition (Sim-ADR) system simultaneously perform defect detection and defect instance segmentation. The accuracy of the defect detection system is more than 87% on a testing set included 416 images.