ArticleNanophotonics (Berlin, Germany)2023
A deep neural network for general scattering matrix.
Article in Nanophotonics (Berlin, Germany), 2023. 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.
- Deep neural networks for inverse design of multimode integrated gratings with simultaneous amplitude and phase control.Nanophotonics (Berlin, Germany) · 2025Article
- Electromagnetic metamaterial agent.Light, science & applications · 2025Article
- Asymmetric imaging through engineered Janus particle obscurants using a Monte Carlo approach for highly asymmetric scattering media.Scientific reports · 2024Article
- Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces.Research (Washington, D.C.) · 2024Article
- A newcomer's guide to deep learning for inverse design in nano-photonics.Nanophotonics (Berlin, Germany) · 2023Review
- Special issue: Metamaterials and plasmonics in Asia, a tribute to Byoungho Lee.Nanophotonics (Berlin, Germany) · 2023Article
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Authors and funding
6 authors.
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Abstract
The scattering matrix is the mathematical representation of the scattering characteristics of any scatterer. Nevertheless, except for scatterers with high symmetry like spheres or cylinders, the scattering matrix does not have any analytical forms and thus can only be calculated numerically, which requires heavy computation. Here, we have developed a well-trained deep neural network (DNN) that can calculate the scattering matrix of scatterers without symmetry at a speed thousands of times faster than that of finite element solvers. Interestingly, the scattering matrix obtained from the DNN inherently satisfies the fundamental physical principles, including energy conservation, time reversal and reciprocity. Moreover, inverse design based on the DNN is made possible by applying the gradient descent algorithm. Finally, we demonstrate an application of the DNN, which is to design scatterers with desired scattering properties under special conditions. Our work proposes a convenient solution of deep learning for scattering problems.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.