AI RESEARCH
Physics-Based Flow Matching for Full-Field Prediction of Silicon Photonic Devices
arXiv CS.LG
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ArXi:2605.06929v1 Announce Type: cross Designing photonic integrated circuits requires accurate electromagnetic field simulations, which remain computationally expensive even for simple device geometries. We present PIC-Flow, a generative neural surrogate that predicts electromagnetic field distributions for photonic devices given their geometry and operating wavelength as an alternative to costly finite-difference time-domain (FDTD) simulations.