Every reference with a DOI in the deposited reference list resolved to a known
work in Crossref or DataCite at the dated check, and none carried a retraction,
withdrawal, or removal notice.
The 124 checked references that resolve
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resolves10.1063/1.1862783Two-dimensional photonic crystals designed by evolutionary algorithms
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resolves10.1515/nanoph-2018-0183Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale
resolves10.1364/OE.27.005874Optimisation of colour generation from dielectric nanostructures using reinforcement learning
resolves10.1515/nanoph-2019-0330Simulator‐based training of generative neural networks for the inverse design of metasurfaces
resolves10.1002/adma.201901111Probabilistic Representation and Inverse Design of Metamaterials Based on a Deep Generative Model with Semi‐Supervised Learning Strategy
resolves10.1515/nanoph-2019-0117Designing nanophotonic structures using conditional deep convolutional generative adversarial networks
resolves10.1364/OE.387504Topological encoding method for data-driven photonics inverse design
resolves10.1021/acs.nanolett.9b03971Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures
resolves10.1109/22.721152Reverse modeling of microwave circuits with bidirectional neural network models
resolves10.1109/22.643868Design and optimization of CPW circuits using EM-ANN models for CPW components
resolves10.1002/mop.28214Optimization of FSS with Sierpinski island fractal elements using population-based search algorithms and MLP neural network
resolves10.1109/TMTT.2003.820897EM-Based Optimization of Microwave Circuits Using Artificial Neural Networks: The State-of-the-Art
resolves10.1109/LPT.2012.2215846Prediction of Dispersion Relation and PBGs in 2-D PCs by Using Artificial Neural Networks
resolves10.1364/OE.27.036414Machine learning approach for computing optical properties of a photonic crystal fiber
resolves10.1109/JLT.2018.2856364Computing Optical Properties of Photonic Crystals by Using Multilayer Perceptron and Extreme Learning Machine
resolves10.1364/PRJ.7.000368Efficient spectrum prediction and inverse design for plasmonic waveguide systems based on artificial neural networks
resolves10.1016/j.asoc.2017.12.043Towards an integrated evolutionary strategy and artificial neural network computational tool for designing photonic coupler devices
resolves10.1364/JOSAB.36.000999Design and optimization of optical passive elements using artificial
neural networks
resolves10.1109/JSTQE.2018.2885486An Open-Source Artificial Neural Network Model for Polarization-Insensitive Silicon-on-Insulator Subwavelength Grating Couplers
resolves10.1126/sciadv.aar4206Nanophotonic particle simulation and inverse design using artificial neural networks
resolves10.1515/nanoph-2020-0194Exploiting deep learning network in optical chirality tuning and manipulation of diffractive chiral metamaterials
resolves10.1038/s41378-019-0069-yFinding the optical properties of plasmonic structures by image processing using a combination of convolutional neural networks and recurrent neural networks
resolves10.1364/OE.27.0A1030Training artificial neural network for optimization of nanostructured VO<sub>2</sub>-based smart window performance
resolves10.1039/C9NR01315FSmart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network
resolves10.1063/1.5094838Inverse design of photonic topological state via machine learning
resolves10.1364/OE.398926Mapping the design space of photonic topological states via deep learning
resolves10.1109/22.390193A neural network modeling approach to circuit optimization and statistical design
resolves10.1364/OL.387404Inverse design of plasmonic metasurfaces by convolutional neural network
resolves10.1021/acsami.9b05857Simultaneous Inverse Design of Materials and Structures via Deep Learning: Demonstration of Dipole Resonance Engineering Using Core–Shell Nanoparticles
resolves10.1002/sam.11161A survey on unsupervised outlier detection in high‐dimensional numerical data
resolves10.1038/s41467-019-12698-1Mapping the global design space of nanophotonic components using machine learning pattern recognition
resolves10.1002/aic.690370209Nonlinear principal component analysis using autoassociative neural networks
resolves10.1038/s41524-020-0276-yDeep learning approach based on dimensionality reduction for designing electromagnetic nanostructures
resolves10.1063/1.5134792Machine-learning-assisted metasurface design for high-efficiency thermal emitter optimization
resolves10.1002/andp.201700302Freeform Metagratings Based on Complex Light Scattering Dynamics for Extreme, High Efficiency Beam Steering
resolves10.1002/adom.201700645Periodic Dielectric Metasurfaces with High‐Efficiency, Multiwavelength Functionalities
resolves10.1515/nanoph-2019-0368Implementation of on‐chip multi‐channel focusing wavelength demultiplexer with regularized digital metamaterials
resolves10.1021/acsphotonics.0c01202Design Space Reparameterization Enforces Hard Geometric Constraints in Inverse-Designed Nanophotonic Devices
resolves10.1515/nanoph-2020-0407Multiobjective and categorical global optimization of photonic structures based on ResNet generative neural networks
resolves10.1021/acsphotonics.9b00706Benchmarking Five Global Optimization Approaches for Nano-optical Shape Optimization and Parameter Reconstruction
resolves10.1016/j.jcp.2018.10.045Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
resolves10.1364/OE.388378MetaNet: a new paradigm for data sharing in photonics research
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