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

arXiv: https://arxiv.org/abs/2501.00742 · HTML: https://mlsanity.com/paper/2501.00742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 5 of 5)

- [DeepReach: A Deep Learning Approach to High-Dimensional Reachability](https://mlsanity.com/paper/2011.02082/) (2020)
- [Experimentally realized in situ backpropagation for deep learning in nanophotonic neural networks](https://mlsanity.com/paper/2205.08501/) (2022)
- [Tensor-Compressed Back-Propagation-Free Training for (Physics-Informed) Neural Networks](https://mlsanity.com/paper/2308.09858/) (2023)
- [TT-PINN: A Tensor-Compressed Neural PDE Solver for Edge Computing](https://mlsanity.com/paper/2207.01751/) (2022)
- [Real-Time FJ/MAC PDE Solvers via Tensorized, Back-Propagation-Free Optical PINN Training](https://mlsanity.com/paper/2401.00413/) (2023)

## Abstract (arXiv)

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.