Physics-Informed Graph Neural Network for Inverse Design of Integrated Photonic Biosensors
In this work, we propose a physics-informed graph neural network (PI-GNN) framework for the inverse design of a microring resonator biosensor operating in the 1550 nm band. By representing the photonic structure as a graph and embedding resonance-based physical constraints directly into the learning objective, the model captures both structural connectivity and underlying electromagnetic principles.
If you use this code, please cite: Torabi, Y., Ekhteraei, A., & Khajezadeh, M. (2026). Physics-Informed Graph Neural Network for Inverse Design of Integrated Photonic Biosensors. ArXiv. https://arxiv.org/abs/2602.19082
