DNN4Photonics objectives

• Innovative methodologies using Deep Neural Networks (DNNs) for modeling and optimizing large-scale photonic structures
• Rapid and reliable surrogates for complex simulations, and efficient inverse design strategies
• Applications to the design of large-scale metasurfaces and metalenses

DNN4Photonics corresponds to two complementary projects: an Inria Associate Team that was initiated in January 2024, and a collaborative research project funded by ANR and FAPESP that has started in January 2025.

Overall, we aim at studying and developing disruptive approaches based on Deep Neural Networks for modeling and shaping the interactions of optical waves with matter when the latter is structured at the subwavelength scale. Electronic devices that exploit these interactions in their design are called photonic devices and the science that studies nanoscale light-matter interactions is referred as nanophotonics. Instead of solely resorting to classical numerical methods such as the finite element method for simulating these interactions, we propose to investigate data-driven Deep Learning methodologies for two main objectives: (1) devising fast and reliable surrogates of light wave propagation in the general case of three-dimensional spatial domains with complex scattering objects and, (2) devising inverse design strategies of photonic devices with unprecedented properties.