ArticleLight, science & applications2025
Multi-wavelength optical information processing with deep reinforcement learning.
Article in Light, science & applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The trial behind it
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Who cites it
8 citing papers in PubMed.
- Emerging integrated photonic neural network technologies for artificial intelligence: from devices to systems.National science review · 2026Review
- AI-enhanced inverse design of photonic crystal fiber optical modulator using deep reinforcement learning technique.Scientific reports · 2026Article
- Physics-informed neural Volterra compensation enabling over 2600× efficiency improvement in 12,057-km ultra-long-haul coherent transmission.Communications engineering · 2026Article
- Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Optimization of broadband metamaterial absorber using twin delayed deep deterministic policy gradient reinforcement learning technique.Scientific reports · 2026Article
- Model-free optical processors using in situ reinforcement learning with proximal policy optimization.Light, science & applications · 2026Article
- Efficient nanophotonic devices optimization using deep neural network trained with physics-based transfer learning methodology.Scientific reports · 2025Article
- MediONN: an integrated photonic chip optical neural network for deep learning-based semantic segmentation in early detection of pancreatic cancer.Biomedical optics express · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Multi-wavelength optical information processing systems are commonly utilized in optical neural networks and broadband signal processing. However, their effectiveness is often compromised by frequency-selective responses caused by fabrication, transmission, and environmental factors. To mitigate these issues, this study introduces a deep reinforcement learning calibration (DRC) method inspired by the deep deterministic policy gradient training strategy. This method continuously and autonomously learns from the system, effectively accumulating experiential knowledge for calibration strategies and demonstrating superior adaptability compared to traditional methods. In systems based on dispersion compensating fiber, micro-ring resonator array, and Mach-Zehnder interferometer array that use multi-wavelength optical carriers as the light source, the DRC method enables the completion of the corresponding signal processing functions within 21 iterations. This method provides efficient and accurate control, making it suitable for applications such as optical convolution computation acceleration, microwave photonic signal processing, and optical network routing.
Identifiers
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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.