Optimizing hyperparameters of Data-driven simulation-assisted-Physics learned AI (DPAI) model to reduce compounding error

Abstract

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.