Flood simulation is a critical tool for understanding and mitigating the devastating impact of floods on human lives and infrastructure. In recent years, there has been a growing demand for real-time, high-resolution flood simulations to enhance our ability to respond to rapidly evolving flood events. However, achieving such simulations is a formidable computational challenge due to the complex and nonlinear nature of flood dynamics in natural environments. This research presents an approach to real-time high-resolution flood simulation by leveraging physics-informed operator learning. The traditional finite difference or finite element methods used in flood simulations require a massive number of grid points and high computational resources, which can limit their real-time applicability. Our proposed framework combines machine learning techniques with domain-specific physics knowledge to significantly reduce the computational cost while maintaining high resolution. This research contributes to the ongoing efforts to harness the power of physics-informed machine learning to tackle complex environmental problems. By combining the strengths of domain knowledge and data-driven modeling, we open up new possibilities for real-time, high-resolution flood simulation that can significantly enhance our capacity to mitigate the impact of flooding events and protect vulnerable communities.
