ArticleScientific reports2026
Accelerating photonic gas sensor design: machine learning-driven inverse optimization of silicon photonics Bragg gratings.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- ANN-PSO Hybrid ML-Optimization of a Hollow-Disk Resonator-Based Photonic Crystal Optical Sensor for HeLa Cell Tumor Detection.Sensors (Basel, Switzerland) · 2026Article
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3 authors.
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Abstract
Optical gas sensors based on photonic structures offer label-free, real-time detection with high sensitivity, but their design optimization remains computationally expensive. We present a hybrid machine learning framework that automates the inverse design of polymer-filled slot Bragg grating (PSBG) sensors for rapid prototyping across multiple target gases. Our approach combines Optuna-tuned artificial neural networks with a multi-model stacking ensemble (gradient boosting, random forest, XGBoost, ridge regression) to predict optimal structural parameters (grating period, depth, ridge width, slot height) from desired spectral characteristics. The framework incorporates domain-informed preprocessing (polynomial feature expansion, physics-based gas encoding) and per-target weighted meta-learners to ensure design fidelity. Validated on 1,045 design samples for CO
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