Abstract Inverse design in nanophotonics, the computational discovery of structures achieving targeted electromagnetic (EM) responses, has become a key tool for recent optical advances. Traditional intuition‐driven or iterative optimization methods struggle with the inherently high‐dimensional, non‐convex design spaces and the substantial computational demands of EM simulations. Recently, machine learning (ML) has emerged to address these bottlenecks effectively. This review frames ML‐enhanced inverse design methodologies through the lens of representation learning , classifying them into two categories: output‐side and input‐side approaches. Output‐side methods use ML to learn a representation in the solution space to create a differentiable solver that accelerates optimization. Conversely, input‐side techniques employ ML to learn compact, latent‐space representations of feasible device geometries, enabling efficient global exploration through generative models. Each strategy presents unique trade‐offs in data requirements, generalization capacity, and novel design discovery potentials. Hybrid frameworks that combine physics‐based optimization with data‐driven representations help escape poor local optima, improve scalability, and facilitate knowledge transfer. Data efficiency, transferable representations, fabrication‐aware design, faster solvers, and hybrid multiphysics co‐design are emphasized.
Marzban et al. (Fri,) studied this question.
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