Auto-Physics-Encoder: Using Physics-Informed Latent Layer Two-Way Physics Flow for Monitoring Systems with Unobservability

Abstract

With the Internet of Everything (IoE) nowadays, monitoring edge systems is essential for coordinating everything into an IoE web. However, it is hard to monitor edge systems due to limited system information and limited sensors. To infer system information and provide robust monitoring capability, machine learning models were used to approximate mapping rules between different measurements. However, mapping rule learning using traditional machine learning tools is one way only, e.g., from measurement variables to the state vector variables. And, it is hard to be reverted, leading to over-fitting because of inconsistency between the forward and inverse learnings. Hence, we propose a structural deep neural network framework to provide a coherent two-way functional approximation. For physical regularization, we embed network size into the number of variables in the latent layers. We also utilize state sensors in the ‘latent layer’ to guide other latent variables to create state sets. The performance of reconstruction for the two-way mapping rule is validated extensively using test cases in the engineering, physics, and mathematical analysis domain.