Artificial Intelligence in Photonics II

We invite researchers, engineers, industry leaders, and academics to submit original contributions exploring the transformative role of artificial intelligence (AI) in advancing photonics technologies. This interdisciplinary conference aims to bridge cutting-edge AI methodologies with photonics innovations, fostering breakthroughs across the entire spectrum of light-based science and applications. We welcome submissions spanning all areas of photonics enhanced by AI, including but not limited to:

AI-driven photonic materials and devices

• generative AI and inverse design of photonic crystals, metamaterials, and plasmonic systems
• reinforcement learning for dynamic control of reconfigurable optical devices
• AI-enabled defect detection and quality assurance in photonic manufacturing
• autonomous labs for high-throughput photonic material discovery.

AI in optical communication and networks

• machine learning for optical signal processing, fiber nonlinearity mitigation, and network optimization
• intelligent routing, fault detection, and self-healing in optical networks
• AI applications in free-space optics, LiDAR, and 6G wireless-photonic integration
• federated learning for secure distributed optical network management
• digital twin platforms for optical network simulation.

AI-enabled computational imaging

• deep learning for computational microscopy, holography, and hyperspectral imaging
• AI-enhanced photonic sensors for biomedical, environmental, and industrial monitoring
• reinforcement learning in adaptive optics and turbulence compensation
• AI-powered optical coherence tomography (OCT), endoscopy, and diagnostic tools
• machine learning for real-time analysis of bioimaging data and disease prediction
• smart photonic systems for personalized medicine and point-of-care diagnostics.

AI for energy and sustainability systems

• AI-optimized solar cells, light-emitting diodes (LEDs), and energy-efficient photonic designs
• predictive maintenance of photonic energy infrastructure using IoT and edge AI
• lifecycle analysis of AI-photonics systems for sustainable design.

AI applications in quantum systems and neuromorphic engineering

• quantum machine learning for photonic quantum computing and error correction
• AI-driven control of quantum light sources and detectors
• photonic neural networks and brain-inspired optical computing architectures.

AI-powered integrated photonics and silicon photonics

• graph neural networks (GNNs) for topology optimization of photonic integrated circuits
• AI-driven foundry tools for multi-project wafer (MPW) photonic chip design
• self-calibrating photonic systems-on-chip for IoT and edge computing.

Emerging frontiers and societal impact

• AI for attosecond photonics and ultrafast laser pulse shaping
• human-in-the-loop AI for collaborative photonic system design
• regulatory challenges and bias mitigation in AI-photonics applications
• global partnerships for democratizing AI-photonics innovations.