ArticleiScience2025
Adjoint method-based Fourier neural operator surrogate solver for wavefront shaping in tunable metasurfaces.
Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Broadband Electromagnetic Field Prediction at Unseen Wavelengths via Physics-Guided Neural Operators.Nanophotonics (Berlin, Germany) · 2026Article
- Review
- Time-domain adjoint optimization for metalens design toward enhanced broadband efficiency and uniformity.iScience · 2025Article
- Inverse Design of Ultrathin Metamaterial Absorber.Nanomaterials (Basel, Switzerland) · 2025Article
- Development of Energy-Selective Surface for Electromagnetic Protection.Micromachines · 2025Review
- Recent Advances in Electromagnetic Devices: Design and Optimization.Micromachines · 2025Article
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Authors and funding
4 authors.
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Abstract
We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wavefronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations. This approach substantially reduces both computational time and cost in wavefront shaping applications. The proposed solver demonstrates a residual of 0.02 when compared to the normalized figure of merit achieved by the optimized structure obtained through the adjoint method, and its inference time is 887.5 times faster than conventional simulation-based methods. This advancement enables ultra-fast wavefront shaping across a range of applications, including optical wavefront shaping, reconfigurable intelligent metasurfaces, and biomedical imaging.
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