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

TL;DR

This paper introduces a variational auto-encoder based method to control complex physical systems with partial measurements, reducing calibration costs and adapting to real-world perturbations, demonstrated in optical and neuroscience datasets.

Contribution

It presents a novel variational framework that models system behavior and computes inputs for desired outputs using limited measurements, applicable to diverse physical systems.

Findings

Effective control of optical systems demonstrated

Applicable to neuroscience data for system characterization

Reduces need for full system measurements and re-calibration

Abstract

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