ReviewNanophotonics (Berlin, Germany)2022
Advancing statistical learning and artificial intelligence in nanophotonics inverse design.
Review 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.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- MOCLIP: a foundation model for large-scale nanophotonic inverse design.Nature communications · 2026Article
- Virtualization as a New Scaling Law for Semiconductor Devices Beyond Geometric Scaling.Small (Weinheim an der Bergstrasse, Germany) · 2026Article
- Self-Assembly Strategies in Upconversion Nanoparticle-Based Nanocomposites: Structure Designs and Applications.International journal of molecular sciences · 2025Review
- Inverse design of nanophotonic devices enabled by optimization algorithms and deep learning: recent achievements and future prospects.Nanophotonics (Berlin, Germany) · 2025Review
- Portable astronomical observation system based on large-aperture concentric-ring metalens.Light, science & applications · 2025Article
- Neural Network Methods in the Development of MEMS Sensors.Micromachines · 2024Review
- Large-scale photonic inverse design: computational challenges and breakthroughs.Nanophotonics (Berlin, Germany) · 2024Review
- Schrödinger's Red Beyond 65,000 Pixel-Per-Inch by Multipolar Interaction in Freeform Meta-Atom through Efficient Neural Optimizer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Optical Metasurfaces for Energy Conversion.Chemical reviews · 2022Review
- Design of a Miniaturized Wide-Angle Fisheye Lens Based on Deep Learning and Optimization Techniques.Micromachines · 2022Article
- The latest trends in nanophotonics.Nanophotonics (Berlin, Germany) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering.
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What OpenQuestion holds
Registered trials
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.