6 citations · 12 across the 4 of their papers we have counts for
5 papers
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…
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…
Photonic KAN: a Kolmogorov-Arnold network inspired efficient photonic neuromorphic architecture
Yiwei Peng, Sean Hooten, Xinling Yu +7
Kolmogorov-Arnold Networks (KAN) models were recently proposed and claimed to provide improved parameter scaling and interpretability compared to conventional multilayer perceptron…
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…
Tensorized Optical Multimodal Fusion Network
Yequan Zhao, Xian Xiao, Geza Kurczveil +2
We propose the first tensorized optical multimodal fusion network architecture with a self-attention mechanism and low-rank tensor fusion. Simulation results show les…