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

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.

By the end of this workshop, participants will be able to:

• 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.

🛠️ Hands-on:

• Simulation and visualization of plasmonic nanostructure optical responses using Python-based open-source tools.
• Analyze optical properties and visualize electromagnetic response patterns of nanophotonic structures.

🧰 Tools Covered: Python, Google Colab, NumPy, Matplotlib, PyMieScatt, Meep

📅 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.

🛠️ Hands-on:

• Develop an AI model for metasurface property prediction and design optimization.
• Train machine learning workflows to predict optical characteristics and optimize nanophotonic structures.

🧰 Tools Covered: Python, Google Colab, Scikit-learn, TensorFlow, PyTorch, Jupyter Notebook

📅 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.

🛠️ Hands-on:

• AI-assisted optimization of a meta-optical device design workflow.
• Apply computational optimization techniques for improving optical device performance.

🧰 Tools Covered: Python, Google Colab, Meep, LightPipes, SciPy Optimization, OpenCV

• 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

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.