Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
Yequan Zhao
Xian Xiao
Antoine Descos
Yuan Yuan
Xinling Yu
G. Kurczveil
M. Fiorentino
Zheng-Wei Zhang
R. Beausoleil
Abstract
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.
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