Broadband Electromagnetic Field Prediction at Unseen Wavelengths via Physics‐Guided Neural Operators

ABSTRACT

Surrogate solvers that deliver full‐wave accuracy across broad spectral ranges can unlock the rapid design and analysis of nanophotonic devices. We present a physics‐guided neural‐operator surrogate solver that predicts electromagnetic field distributions throughout the visible band from a permittivity map and a query wavelength, including wavelengths unseen during training. Our approach is built on two key ideas: (i) spectral consistency, which formalizes the intrinsic relationship between wavelength‐dependent field variations and spatial frequency, and (ii) a conditional embedding framework that comprises a refined wave prior. We validate the approach on three representative nanophotonic platforms—a single‐layer metasurface (metalenses), a five‐layer volumetric metasurface (spectrum‐splitters), and a freeform waveguide for photonic integrated circuits—achieving full‐wave fidelity while reducing mean field‐prediction error relative to prior operator‐learning baselines by up to 80.3% (single‐layer), 56.8% (multilayer), and 32.3% (waveguide). The solver remains compact (0.43 M parameters; 86.9% fewer than FNO) and accelerates simulation by compared with a finite‐difference frequency‐domain solver while yielding robust broadband generalization. By providing continuous‐wavelength field predictions with full‐wave accuracy, this surrogate solver eliminates a key bottleneck in modeling and inverse design of metalenses, spectrum splitters, and emerging large‐area meta‐optics, offering a drop‐in engine for system‐level co‐optimization in nanophotonics.