PDE Surrogate Modeling

PDE Surrogate Modeling is a surrogate modeling technique based on partial differential equations (PDEs) that aims to replace high-complexity original PDE models with low-dimensional, efficient alternative models, thereby significantly reducing computational costs and enhancing solution efficiency. This method employs a data-driven approach, utilizing machine learning algorithms to learn and approximate the solution space of PDEs. It can achieve fast responses and real-time simulations while maintaining a certain level of accuracy. PDE Surrogate Modeling has significant applications in engineering optimization, real-time control, uncertainty quantification, and other fields, effectively supporting the efficient design and analysis of complex systems.