ArticleScientific reports2019
Deep Neural Network Inverse Design of Integrated Photonic Power Splitters.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
- Optical Scattering Characteristics of Three-Dimensional Targets Based on Transfer Learning PINNs.Micromachines · 2026Article
- Mode Visualization and Control of Complex Lasers Using Neural Networks.ACS photonics · 2025Article
- Review
- A novel intelligent photonic design method enabled by metamaterials and k-nearest neighbor.Nanophotonics (Berlin, Germany) · 2025Article
- Enhancing computational efficiency in topology-optimized mode converters via dynamic update rate strategies.Scientific reports · 2024Article
- Inverse-designed taper configuration for the enhancement of integrated 1 × 4 silicon photonic power splitters.Nanophotonics (Berlin, Germany) · 2024Article
- Design of Ultra-Compact and Multifunctional Optical Logic Gate Based on SbNanomaterials (Basel, Switzerland) · 2024Article
- Sample-efficient inverse design of freeform nanophotonic devices with physics-informed reinforcement learning.Nanophotonics (Berlin, Germany) · 2024Article
- A deep learning method for empirical spectral prediction and inverse design of all-optical nonlinear plasmonic ring resonator switches.Scientific reports · 2024Article
- Deep photonic network platform enabling arbitrary and broadband optical functionality.Nature communications · 2024Article
- Article
- A newcomer's guide to deep learning for inverse design in nano-photonics.Nanophotonics (Berlin, Germany) · 2023Review
- A Quick Method for Predicting Reflectance Spectra of Nanophotonic Devices via Artificial Neural Network.Nanomaterials (Basel, Switzerland) · 2023Article
- Utilizing Mixed Training and Multi-Head Attention to Address Data Shift in AI-Based Electromagnetic Solvers for Nano-Structured Metamaterials.Nanomaterials (Basel, Switzerland) · 2023Article
- Convolutional neural networks for mode on-demand high finesse optical resonator design.Scientific reports · 2023Article
- A Multi-Channel Frequency Router Based on an Optimization Algorithm and Dispersion Engineering.Nanomaterials (Basel, Switzerland) · 2023Article
- Inverse design in quantum nanophotonics: combining local-density-of-states and deep learning.Nanophotonics (Berlin, Germany) · 2023Article
- Deep learning accelerated discovery of photonic power dividers.Nanophotonics (Berlin, Germany) · 2023Article
- Neural Inverse Design of Nanostructures (NIDN).Scientific reports · 2022Article
- Optimization of metamaterials and metamaterial-microcavity based on deep neural networks.Nanoscale advances · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting physical response of an artificially structured material is of particular interest for scientific and engineering applications. Here we use deep learning to predict optical response of artificially engineered nanophotonic devices. In addition to predicting forward approximation of transmission response for any given topology, this approach allows us to inversely approximate designs for a targeted optical response. Our Deep Neural Network (DNN) could design compact (2.6 × 2.6 μm
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Registered trials
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