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Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training

Machine Learning 2025-01-03 v1 Hardware Architecture Optics

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.

Keywords

Cite

@article{arxiv.2501.00742,
  title  = {Experimental Demonstration of an Optical Neural PDE Solver via On-Chip PINN Training},
  author = {Yequan Zhao and Xian Xiao and Antoine Descos and Yuan Yuan and Xinling Yu and Geza Kurczveil and Marco Fiorentino and Zheng Zhang and Raymond G. Beausoleil},
  journal= {arXiv preprint arXiv:2501.00742},
  year   = {2025}
}