Geometry-based operator learning

Geometry-based operator learning is a branch of Deep Operator Networks, focusing on solving multi-geometry problems. This approach learns the mapping between geometric structures and physical fields to enable efficient prediction of physical phenomena under different geometric shapes. Its goal is to provide accurate and fast computational capabilities in complex geometric environments, significantly enhancing the efficiency and accuracy of traditional numerical simulations. It has important applications in engineering design, materials science, and other fields, capable of accelerating the development of new products and optimizing the performance of existing systems.