Generalizing morphologies in dam break simulations using transformer model

The interaction of waves with structural barriers, such as dam breaking, plays a critical role in flood defense and tsunami disasters. In this work, we explore the dynamic changes in wave surfaces impacting various structural shapes—circle, triangle, and square—using deep learning techniques. We introduce the “DamFormer,” a novel transformer-based model designed to learn and simulate these complex interactions. Additionally, we conducted zero-shot experiments to evaluate the model's ability to generalize across different domains. This approach enhances our understanding of fluid dynamics in marine engineering and opens new avenues for advancing computational methods in the field. Our findings demonstrate the potential of deep learning models like the DamFormer to provide significant insights and predictive capabilities in ocean engineering and fluid mechanics.