ArticleNanophotonics (Berlin, Germany)2022
Inverse design of structural color: finding multiple solutions
Article in Nanophotonics (Berlin, Germany), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- AI-enhanced inverse design of photonic crystal fiber optical modulator using deep reinforcement learning technique.Scientific reports · 2026Article
- Design and Optimization of Full-Stokes Hyperspectro-Polarimetric Encoding Metasurfaces Based on Conditional Multi-Task Deep Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Ultraprecision, high-capacity, and wide-gamut structural colors enabled by a mixture probability sampling network.Light, science & applications · 2026Article
- The analysis of generative artificial intelligence technology for innovative thinking and strategies in animation teaching.Scientific reports · 2025Article
- Optical multilayer thin film structure inverse design: From optimization to deep learning.iScience · 2025Review
- Incremental Inverse Design of Desired Soybean Phenotypes.ACS omega · 2024Article
- Nested deep transfer learning for modeling of multilayer thin films.Advanced photonics · 2024Article
- Large-scale photonic inverse design: computational challenges and breakthroughs.Nanophotonics (Berlin, Germany) · 2024Review
- A newcomer's guide to deep learning for inverse design in nano-photonics.Nanophotonics (Berlin, Germany) · 2023Review
- Diffusion probabilistic model based accurate and high-degree-of-freedom metasurface inverse design.Nanophotonics (Berlin, Germany) · 2023Article
- Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities.Nanophotonics (Berlin, Germany) · 2023Article
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Authors and funding
9 authors.
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Abstract
The "one-to-many" problem is a typical challenge that faced by many machine learning aided inverse nanophotonics designs where one target optical response can be achieved by many solutions (designs). Although novel training approaches, such as tandem network, and network architecture, such as the mixture density model, have been proposed, the critical problem of solution degeneracy still exists where some possible solutions or solution spaces are discarded or unreachable during the network training process. Here, we report a solution to the "one-to-many" problem by employing a conditional generative adversarial network (cGAN) that enables generating sets of multiple solution groups to a design problem. Using the inverse design of a transmissive Fabry-Pérot-cavity-based color filter as an example, our model demonstrates the capability of generating an average number of 3.58 solution groups for each color. These multiple solutions allow the selection of the best design for each color which results in a record high accuracy with an average index color difference Δ
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