Machine Learning for Underwater Explosion Prediction: A Comprehensive Review of Methods, Challenges and Future Directions

Underwater explosions (UNDEX) represent a critical threat to the structural integrity of submerged and floating systems across marine, offshore, and civil engineering sectors. Simulating their complex effects-driven by strong fluid-structure interaction (FSI), cavitation, and nonlinear material behavior-typically relies on numerical methods that, although accurate, are computationally expensive and not ideal for rapid predictions in practical scenarios. To overcome these limitations, the integration of Artificial Intelligence (AI) and Machine Learning (ML) into UNDEX research has recently emerged as a promising, albeit still developing, alternative. This paper presents a systematic review of AI and ML applications for predicting structural and fluid responses under underwater blast loading, focusing on developments over the past two decades. After introducing the governing physics of UNDEX events and summarizing the numerical techniques commonly used for dataset generation, the paper p...