Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
Yequan Zhao, Xian Xiao, Antoine Descos, Yuan Yuan, Xinling Yu, Geza Kurczveil, Marco Fiorentino, Zheng Zhang, Raymond G. Beausoleil
arXiv:2501.00742v11 citationsh-index: 58OFC
Originality Synthesis-oriented
AI Analysis
This work addresses PDE solving for science and engineering applications, but it appears incremental as it focuses on experimental implementation of existing methods.
The paper tackled solving partial differential equations (PDEs) by experimentally demonstrating an optical neural solver using on-chip training of physics-informed neural networks, achieving a demonstration of back-propagation-free training on photonic hardware.
Partial differential equation (PDE) is an important math tool in science and engineering. This paper experimentally demonstrates an optical neural PDE solver by leveraging the back-propagation-free on-photonic-chip training of physics-informed neural networks.