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As we look to the next generation of adaptive optics systems, now is the timeto develop and explore the technologies that will allow us to image rockyEarth-like planets; wavefront control algorithms are not only a crucialcomponent of these systems, but can benefit our adaptive optics systems withoutrequiring increased detector speed and sensitivity or more effective andefficient deformable mirrors. To date, most observatories run the workhorse oftheir wavefront control as a classic integral controller, which estimates acorrection from wavefront sensor residuals, and attempts to apply thatcorrection as fast as possible in closed-loop. An integrator of this naturefails to address temporal lag errors that evolve over scales faster than thecorrection time, as well as vibrations or dynamic errors within the system thatare not encapsulated in the wavefront sensor residuals; these errors impacthigh contrast imaging systems with complex coronagraphs. With the rise inpopularity of machine learning, many are investigating applying modern machinelearning methods to wavefront control. Furthermore, many linear implementationsof machine learning methods (under varying aliases) have been in developmentfor wavefront control for the last 30-odd years. With this work we definemachine learning in its simplest terms, explore the most common machinelearning methods applied in the context of this problem, and present a reviewof the literature concerning novel machine learning approaches to wavefrontcontrol.

Tempestas ex machina: A review of machine learning methods for wavefront control