Artificial Intelligence-Driven Metasurfaces Spanning Multidimensional Light Field Control and Free Space Computing

Metasurfaces exploit subwavelength scattering elements to manipulate light with a level of flexibility that is difficult to achieve using conventional optical platforms, making them promising building blocks for next-generation photonic systems. Yet the increasing dimensionality of metasurface design spaces and the demand for multifunctional responses have exposed the limitations of traditional intuition-led design approaches. In this Review, we survey the emergence of artificial intelligence (AI)-empowered metasurfaces across three major themes: inverse design, multidimensional optical-field control, and free-space optical computing. We first summarize the fundamental principle of optical field manipulation and the algorithmic approaches to metasurface design, including stochastic optimization, deep neural networks, and generative models, with emphasis on their capabilities in rapid performance prediction and inverse structural discovery. We next discuss artificial intelligence-assisted strategies for engineering multiple spatial, spectral, and polarization degrees of freedom in free space. We then highlight the role of AI-empowered metasurface architectures in optical information processing and computation. Together, these developments point to a powerful framework for integrating machine intelligence with meta-optics, with implications for autonomous photonic systems and high-capacity optical computing.