Research Paper#Nanophotonics, Machine Learning, Neural Networks, Optimization🔬 ResearchAnalyzed: Jan 3, 2026 16:03
NEAT for Optimizing Chiral Photonic Metasurfaces
Published:Dec 29, 2025 15:55
•1 min read
•ArXiv
Analysis
This paper introduces a novel application of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm within a deep-learning framework for designing chiral metasurfaces. The key contribution is the automated evolution of neural network architectures, eliminating the need for manual tuning and potentially improving performance and resource efficiency compared to traditional methods. The research focuses on optimizing the design of these metasurfaces, which is a challenging problem in nanophotonics due to the complex relationship between geometry and optical properties. The use of NEAT allows for the creation of task-specific architectures, leading to improved predictive accuracy and generalization. The paper also highlights the potential for transfer learning between simulated and experimental data, which is crucial for practical applications. This work demonstrates a scalable path towards automated photonic design and agentic AI.
Key Takeaways
- •Integrates NEAT into a deep-learning framework for designing chiral metasurfaces.
- •NEAT automates neural network architecture evolution, eliminating manual tuning.
- •Achieves similar or improved predictive accuracy and generalization compared to traditional methods.
- •Demonstrates transfer learning between simulated and experimental data.
- •Provides a scalable path towards automated photonic design and agentic AI.
Reference
“NEAT autonomously evolves both network topology and connection weights, enabling task-specific architectures without manual tuning.”