Incremental Distillation Physics-Informed Neural Network (IDPINN) Accurately Models the Evolution of Optical Solitons

Optical solitons play an important role in long-distance, high-capacity communications. To enhance the precision of soliton dynamics modeling, the authors combine incremental learning techniques with physics-informed neural network. The novel model employs a process of knowledge distillation and fine-tuning to continually integrate fresh physical information into the neural network. This iterative approach leads to a constant improvement in the network’s ability to extract features. The authors conduct experiments on three solitons, and the new method significantly reduces the error compared to the general physics-informed neural network. The modeling approach put forward in this research is anticipated to contribute to the advancement of all-optical computing research and facilitate the development of novel fiber optic communication systems.