mlsanityData to 7 Oct 2026

A neural operator-based surrogate solver for free-form electromagnetic inverse design

arXiv 2302.01934 ↗First posted 4 Feb 2023physics.comp-ph cs.LG physics.opticscited by 18

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Abstract (arXiv)

Neural operators have emerged as a powerful tool for solving partial differential equations in the context of scientific machine learning. Here, we implement and train a modified Fourier neural operator as a surrogate solver for electromagnetic scattering problems and compare its data efficiency to existing methods. We further demonstrate its application to the gradient-based nanophotonic inverse design of free-form, fully three-dimensional electromagnetic scatterers, an area that has so far eluded the application of deep learning techniques.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 22 of its 73 references that are in the map. First posted on arXiv 4 Feb 2023.

Builds on

22 of 73 references.

References · 22

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10 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 6; 2025: 4; 2026: 7.

18 papers.

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  1. 2024DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Trainingcited by 137
  2. 2024Accelerating Data Generation for Neural Operators via Krylov Subspace Recyclingcited by 12
  3. 2025Mixture-of-Experts Operator Transformer for Large-Scale PDE Pre-Trainingcited by 10
  4. 2025Inverse Design in Nanophotonics via Representation Learningcited by 7
  5. 2024Learning Electromagnetic Metamaterial Physics With ChatGPTcited by 6
  6. 2024PIC2O-Sim: A Physics-Inspired Causality-Aware Dynamic Convolutional Neural Operator for Ultra-Fast Photonic Device FDTD Simulationcited by 2
  7. 2024PearSAN: A Machine Learning Method for Inverse Design using Pearson Correlated Surrogate Annealingcited by 1
  8. 2026Ambient Physics: Training Neural PDE Solvers with Partial Observationscited by 1
  9. 2023Photonic Structures Optimization Using Highly Data-Efficient Deep Learning: Application To Nanofin And Annular Groove Phase Maskscited by 0
  10. 2024Generalizing Deep Surrogate Solvers for Broadband Electromagnetic Field Prediction at Unseen Wavelengthscited by 0
  11. 2025Operator Learning for Schr"odinger Equation: Unitarity, Error Bounds, and Time Generalizationcited by 0
  12. 2025Optimizing Predictive Maintenance in Intelligent Manufacturing: An Integrated FNO-DAE-GNN-PPO MDP Frameworkcited by 0
6 more
  1. 2026Gradient-Informed Bayesian and Interior Point Optimization for Efficient Inverse Design in Nanophotonicscited by 0
  2. 2026Alpha-RF: Automated RF-Filter-Circuit Design with Neural Simulator and Reinforcement Learningcited by 0
  3. 2026Harnessing AI for Inverse Partial Differential Equation Problems: Past, Present, and Prospectscited by 0
  4. 2026Fast Reconstruction of Exact Maxwell Dynamics from Sparse Datacited by 0
  5. 2026Inductively Scalable, Single-Step Neural Surrogates for Wave-Scattering Inverse Problemscited by 0
  6. 2026Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environmentscited by 0