Physics-Constrained Generative Inverse Design or Photonic Metasurfaces via Differentiable Electromagnetic Learning

Inverse photonic design remains computationally expensive due to the complexity of solving Maxwell’s equations over high-dimensional geometric spaces. Conventional inverse design approaches rely on iterative electromagnetic simulation and local optimization methods that often suffer from poor scalability, convergence instability, and fabrication incompatibility. This work introduces NEUROPHOTON, a conceptual physics-constrained generative framework for inverse photonic design integrating generative artificial intelligence, differentiable electromagnetic learning, and Maxwell-consistent latent representations. Instead of directly optimizing geometric parameters through deterministic search, the proposed framework reformulates inverse photonic design as constrained generative inference within a physically admissible latent manifold. Simulation-based evaluations suggest improved convergence efficiency, increased valid solution generation, and reduced computational cost relative to conventional optimization pipelines. The proposed framework establishes a conceptual foundation for generative physical design systems in computational photonics and suggests a scalable pathway toward AI-assisted photonic computing architectures.