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cs.LG2025
Scalable Back-Propagation-Free Training of Optical Physics-Informed Neural Networks
Yequan Zhao, Xinling Yu, Xian Xiao +6
Physics intelligence and digital twins often require rapid and repeated performance evaluation of various engineering systems (e.g. robots, autonomous vehicles, semiconductor chips…
cs.LG2025★ 1 cited
Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training
Yequan Zhao, Xian Xiao, Antoine Descos +6
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…
cs.LG2024★ 4 cited
Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training
Yequan Zhao, Xian Xiao, Xinling Yu +5
Solving partial differential equations (PDEs) numerically often requires huge computing time, energy cost, and hardware resources in practical applications. This has limited their…