Collaborative Photonic Crystal Fiber Property Optimization: A New Paradigm for Reverse Design

The reverse design of photonic crystal fibers (PCFs) using AI has received significant attention in recent years. To quickly optimize the fundamental optical properties of PCFs and obtain the optimal design combination, researchers have favored machine learning algorithms. However, relying solely on the knowledge provided by a few experts has limited the versatility and performance of these models. To address these issues, we propose a collaborative PCF property optimization framework as a new paradigm for reverse design. This framework allows experts from different institutions to share their design knowledge and collaboratively optimize the fundamental optical properties of PCFs without direct data interaction. Additionally, this framework can adapt to low-resourced computing devices that only use a small dataset without any data augmentation methods to achieve excellent optimization performance. Our proposed framework provides a novel and practical platform for global collaboration among research institutions for the reverse design of micro-structured fibers.