ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2021
Tackling Photonic Inverse Design with Machine Learning.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
36 citing papers in PubMed.
- Intelligent Algorithm-Assisted Indirect Absorption Spectroscopy for Trace Gas Sensing.Sensors (Basel, Switzerland) · 2026Review
- Machine Learning Models for Predicting Key Performance Characteristics of High-Temperature THz Quantum Cascade Lasers.Nanomaterials (Basel, Switzerland) · 2026Article
- MxDiffusion: A Physics-Aware Maxwell's Law-Guided Diffusion Model Strategy for Inverse Photonic Metasurface Design.Nano letters · 2026Article
- Inverse Design of Optical Color Routers with Improved Fabrication Compatibility.Nanomaterials (Basel, Switzerland) · 2026Article
- Adjoint-Driven Inverse Design of a Quad-Spectral Metasurface Router for RGB-NIR Sensing.Nanomaterials (Basel, Switzerland) · 2025Article
- Tunable holographic metasurfaces for augmented and virtual reality.Nanophotonics (Berlin, Germany) · 2025Article
- Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization.Nanophotonics (Berlin, Germany) · 2025Review
- Augmented Bayesian Data Selection: Improving Machine Learning Predictions of Bragg Grating Spectra.Sensors (Basel, Switzerland) · 2025Article
- Multi-Dimensional Multiplexed Metasurface for Multifunctional Near-Field Modulation by Physics-Driven Intelligent Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Optical multilayer thin film structure inverse design: From optimization to deep learning.iScience · 2025Review
- Inverse design of a valley-Hall photonic topological insulator based on tandem residual neural networks.iScience · 2025Article
- All-optical analog differential operation and information processing empowered by meta-devices.Nanophotonics (Berlin, Germany) · 2025Review
- Machine learning assisted plasmonic metascreen for enhanced broadband absorption in ultra-thin silicon films.Light, science & applications · 2025Article
- AI in SERS sensing moving from discriminative to generative.Npj biosensing · 2025Review
- Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models.PeerJ. Computer science · 2025Article
- Highly accurate, efficient, and fabrication tolerance-aware nanostructure prediction for high-performance optoelectronic devices.Scientific reports · 2024Article
- Physics-Informed Inverse Design of Programmable Metasurfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Predicting refractive index of inorganic compounds using machine learning.Scientific reports · 2024Article
- Reverse Design of Pixel-Type Micro-Polarizer Arrays to Improve Polarization Image Contrast.Micromachines · 2024Article
- Distribution of Single-Particle Resonances Determines the Plasmonic Response of Disordered Nanoparticle Ensembles.ACS nano · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning, as a study of algorithms that automate prediction and decision-making based on complex data, has become one of the most effective tools in the study of artificial intelligence. In recent years, scientific communities have been gradually merging data-driven approaches with research, enabling dramatic progress in revealing underlying mechanisms, predicting essential properties, and discovering unconventional phenomena. It is becoming an indispensable tool in the fields of, for instance, quantum physics, organic chemistry, and medical imaging. Very recently, machine learning has been adopted in the research of photonics and optics as an alternative approach to address the inverse design problem. In this report, the fast advances of machine-learning-enabled photonic design strategies in the past few years are summarized. In particular, deep learning methods, a subset of machine learning algorithms, dealing with intractable high degrees-of-freedom structure design are focused upon.
Indexed as
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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.