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Auto-Physics-Encoder: Using Physics-Informed Latent Layer Two-Way Physics Flow for Monitoring Systems with Unobservability
Proceedings of The 14th Asian Conference on Machine
Learning, PMLR 189:958-973, 2023.
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.