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To characterize a physical system to behave as desired, either its underlyinggoverning rules must be known a priori or the system itself be accuratelymeasured. The complexity of full measurements of the system scales with itssize. When exposed to real-world conditions, such as perturbations ortime-varying settings, the system calibrated for a fixed working conditionmight require non-trivial re-calibration, a process that could be prohibitivelyexpensive, inefficient and impractical for real-world use cases. In this work,we propose a learning procedure to obtain a desired target output from aphysical system. We use Variational Auto-Encoders (VAE) to provide a generativemodel of the system function and use this model to obtain the required input ofthe system that produces the target output. We showcase the applicability ofour method for two datasets in optical physics and neuroscience.

Variational framework for partially-measured physical system control: examples of vision neuroscience and optical random media