Optical Design, Analysis, and Calibration using partial derivative Lux

partial derivative Lux is a newly developed optical modelling framework deigned to harness the tools underpinning the modern machine learning revolution and directly apply them to optics. Both neural networks and optical systems map an input vector to some output vector employing a series of intermediary linear transformations and non-linear matrix operations. This isomorphism allows for optical models to be directly constructed within existing automatic differentiation libraries. partial derivative Lux exploits this relationship harnessing automatic differentiation libraries to create a naively end-to-end fully differentiable optical modelling framework. This may precipitate a paradigm shift in the power and utility of these optical models, opening the possibility to entirely novel algorithms and approaches. This manuscript explores some of the many ways to harness the potential of these codes, particularly focusing on the application example provided by the Toliman space telescope mission.