ArticleNanophotonics (Berlin, Germany)2024
Sample-efficient inverse design of freeform nanophotonic devices with physics-informed reinforcement learning.
Article in Nanophotonics (Berlin, Germany), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Q-LEAP: Millisecond Hyperdimensional Optimization for Full-Spectrum Optical Metamaterials.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Deep neural networks for inverse design of multimode integrated gratings with simultaneous amplitude and phase control.Nanophotonics (Berlin, Germany) · 2025Article
- All-optical analog differential operation and information processing empowered by meta-devices.Nanophotonics (Berlin, Germany) · 2025Review
- Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects.Nanophotonics (Berlin, Germany) · 2025Review
- Large-scale photonic inverse design: computational challenges and breakthroughs.Nanophotonics (Berlin, Germany) · 2024Review
- Editorial on special issue "Metamaterials and Plasmonics in Asia".Nanophotonics (Berlin, Germany) · 2024Article
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Authors and funding
9 authors.
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Abstract
Finding an optimal device structure in the vast combinatorial design space of freeform nanophotonic design has been an enormous challenge. In this study, we propose physics-informed reinforcement learning (PIRL) that combines the adjoint-based method with reinforcement learning to improve the sample efficiency by an order of magnitude compared to conventional reinforcement learning and overcome the issue of local minima. To illustrate these advantages of PIRL over other conventional optimization algorithms, we design a family of one-dimensional metasurface beam deflectors using PIRL, exceeding most reported records. We also explore the transfer learning capability of PIRL that further improves sample efficiency and demonstrate how the minimum feature size of the design can be enforced in PIRL through reward engineering. With its high sample efficiency, robustness, and ability to seamlessly incorporate practical device design constraints, our method offers a promising approach to highly combinatorial freeform device optimization in various physical domains.
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