AI-Driven Intelligent Metasurfaces: Inverse Design, Plasmonics & Meta-Optics

The integration of Artificial Intelligence with nanophotonics is transforming the way advanced optical devices are designed and engineered. This workshop provides an in-depth understanding of AI-driven intelligent metasurfaces, inverse design methodologies, plasmonic nanostructures, and meta-optical systems. Participants will explore how machine learning and computational approaches are enabling the discovery and optimization of complex nanoscale optical structures beyond conventional design limitations. The workshop covers fundamental concepts of metasurface engineering, plasmonic light–matter interactions, AI-based optimization strategies, and emerging applications in imaging, sensing, communication, and photonic technologies. Through research-oriented discussions and hands-on computational sessions using open-source tools, participants will gain practical exposure to AI-assisted workflows for designing and analyzing next-generation photonic devices.

To provide researchers, academicians, and industry professionals with a comprehensive understanding of AI-enabled metasurface engineering , focusing on inverse design, plasmonic nanophotonics, and meta-optical applications for emerging technologies.

• Understand the fundamentals of metasurfaces, metamaterials, and meta-optics.
• Explore the role of AI in accelerating nanophotonic device discovery and optimization.
• Learn machine learning approaches for predicting optical properties and designing metasurface structures.
• Understand plasmonic phenomena and their applications in sensing, imaging, and optical technologies.
• Gain insights into AI-based inverse design workflows for next-generation optical devices.
• Apply open-source computational tools for simulation and optimization of nanophotonic systems.
• Explore current research trends and future opportunities in intelligent photonics.

📅 Day 1: Fundamentals of Intelligent Metasurfaces & Plasmonic Nanophotonics

• Focus: Understanding metasurfaces, plasmonic nanostructures, meta-optics, and computational approaches for advanced optical design. Introduction to Metasurfaces, Metamaterials, and Meta-Optics. Design principles of meta-atoms and subwavelength optical structures. Electromagnetic wave manipulation through phase, amplitude, and polarization control. Fundamentals of Plasmonics and Surface Plasmon Resonance (SPR). Plasmonic nanostructures for sensing, imaging, and optical applications. Overview of computational approaches for metasurface design and optical response analysis.

📅 Day 2: AI-Based Inverse Design & Optimization of Metasurfaces

• Focus: Applying Artificial Intelligence and machine learning approaches for metasurface prediction, optimization, and inverse photonic design. Fundamentals of AI-driven photonic design workflows. Understanding forward modelling versus inverse design approaches in nanophotonics. Machine Learning models for predicting optical responses of metasurface structures. Deep Learning architectures for metasurface optimization and property prediction. Generative AI approaches for nanophotonic structure discovery and automated design. Bayesian optimization, genetic algorithms, and physics-guided AI methods for photonic engineering.

📅 Day 3: AI-Enhanced Meta-Optics & Future Photonic Applications

• Focus: Exploring AI-enhanced meta-optical systems, computational imaging, and future applications of intelligent photonic technologies. AI-designed metalenses and flat optical systems. Computational imaging using metasurface–AI integration. Intelligent optical devices for sensing, communication, and imaging applications. Reconfigurable metasurfaces for next-generation optical technologies. Research trends in AI-powered nanophotonics and optical engineering. Complete workflow from AI-based design to experimental validation of photonic devices.

• Participants will gain:
• ✅ Understanding of intelligent metasurface architectures and their applications.
• ✅ Knowledge of AI-driven inverse design approaches for optical and photonic systems.
• ✅ Practical exposure to machine learning workflows for metasurface optimization.
• ✅ Insights into plasmonic nanostructures, metalenses, and computational meta-optics.
• ✅ Experience with open-source simulation and AI tools used in nanophotonics research.
• ✅ Ability to identify research opportunities in AI-powered optical engineering and photonic technologies.

Nanophotonics, Metamaterials & Plasmonics researchers Optical Engineering and Photonics professionals Materials Science & Computational Physics researchers Faculty members exploring AI-driven optical design approaches Nanotechnology and Applied Physics researchers Electrical & Electronics Engineering scholars Materials Engineering and Optics students AI applications in scientific research enthusiasts Semiconductor and photonic technology professionals Optical device and sensor developers Photonic integrated circuit researchers Imaging, communication & AI engineering professionals