mlsanityData to 7 Oct 2026

Will Accurate Fields Mislead Photonic Design? FromGlobal Accuracy to Port Readout

arXiv 2606.03038 ↗First posted 2 Jun 2026cs.LG physics.comp-ph physics.opticscited by 0

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Abstract (arXiv)

Neural field surrogates can accelerate photonic design loops, but a surrogate that looks accurate in global field error can still mis-rank candidate devices when the final decision depends on localized output-port readouts. This risk is acute in propagation-dominated MMI splitters and couplers, where port power, splitting, phase, and coupling are determined by accumulated modal interference and output-window aggregation rather than by average field similarity alone. We study this field-to-design mismatch through a Field/Mediator/Readout view that separates dense complex-field error from propagation-profile and output-window errors before port aggregation. To align the surrogate with this chain, we propose PaNO, a propagation-aligned neural operator that keeps the full-field prediction interface while organizing latent states around local boundary structure, transverse modal content, axial propagation, and cross-mode interaction. We also evaluate PaNO-R2, an output-aware feedback variant for residual field components near the port region. On a 15-wavelength tunable 3×3 MMI benchmark with 4608 held-out fields, PaNO lowers NeurOLight's port-power error from 0.2018 to 0.0739 despite slightly higher cMAE, showing that global field accuracy alone is not sufficient for design-relevant readout fidelity. PaNO-R2 attains the best cMAE, propagation-profile error, output-profile error, and port-power error, reducing NeurOLight's port-power and output-profile errors by 72.7% and 72.5%.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 27 references that are in the map. First posted on arXiv 2 Jun 2026.

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10 of 27 references.

References · 10

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
  3. 2020Fourier Neural Operator for Parametric Partial Differential Equationscited by 2,806
  4. 2021Efficiently Modeling Long Sequences with Structured State Spacescited by 2,424
  5. 2019DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorscited by 1,965
  6. 2021Choose a Transformer: Fourier or Galerkincited by 333
  7. 2023GNOT: A General Neural Operator Transformer for Operator Learningcited by 321
  8. 2021Factorized Fourier Neural Operatorscited by 237
  9. 2020Photonics for artificial intelligence and neuromorphic computingcited by 111
  10. 2022NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulationcited by 17

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