A Physics-informed and data-driven deep learning approach for wave propagation and its scattering characteristics

Understanding the propagation of waves and their scattering characteristics is critical in various scientific and engineering domains. While the majority of present work is based on numerical approaches, their high computational cost and discontinuity in the entire engineering workflow raise the need to resolve obstacles for fully utilizing the methods in an interactive and end-to-end manner. In this study, we propose a deep learning approach that can simulate the wave propagation and scattering phenomena precisely and efficiently. In particular, we present methods of incorporating physics-based knowledge into the deep learning framework to give the learning process strong inductive biases regarding wave propagation and scattering behaviors. We demonstrate that the proposed method can successfully produce physically valid wave field trajectories induced by random scattering objects. We show that the proposed physics-informed strategy exhibits significantly improved prediction results than purely data-driven methods through quantitative and qualitative evaluation from various angles. Subsequently, we assess the computational efficiency of the proposed method as a neural engine, showing that the proposed approach can significantly accelerate the scientific simulation process compared to the numerical method. Our study delivers the potential of the proposed physics-informed approach to be utilized for real-time, accurate, and interactive scientific analyses in a wide variety of engineering and application disciplines.