Extrapolating from lens design databases using deep learning

We propose for the first time a deep learning approach in assisting lens designers to find a lens design starting point. Using machine learning, lens design databases can be expanded in a continuous way to produce high-quality starting points from various optical specifications. A deep neural network (DNN) is trained to reproduce known forms of design (supervised training) and to jointly optimize the optical performance (unsupervised training) for generalization. In this work, the DNN infers high-performance cemented and air-spaced doublets that are tailored to diverse desired specifications after being fed with reference designs from the literature. The framework can be extended to lens systems with more optical surfaces.