Abstract

Data-driven methods to assist lens design have recently begun to emerge, in particular under the form of lens design extrapolation: using machine learning, the features of successful lens design forms can be extracted, then recombined to create new designs. Here, we discuss the core aspects and next challenges of the LensNet framework, a deep learning-enabled tool that leverages lens design extrapolation as a more powerful alternative to lens design databases when searching for starting points. We also propose to borrow ideas and tools from the practice of machine learning and deep learning, and integrate them into standard lens design optimization. Namely, we recommend using automatic differentiation to power ray tracing engines, along with considering recent and powerful first-order gradient-based optimizers, and using data-driven glass models that are more suited for optimization than traditional variables.

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