The result is a patchwork of incomplete and irregular observations, and the underlying energy flow structure of the system becomes hidden.
Liang’s framework, SPEL-VFL — short for physics-embedded latent energy flow learning — is designed specifically for this gap.
What makes the work particularly timely is its explicit application to hydrogen energy transportation and hydrogen intelligent mobility systems.
Subject of Research: Physics-embedded latent machine learning for hydrogen energy flow reconstruction under sparse IoT sensing Article Title: Physics-embedded latent hydrogen energy flow learning under sparse sensing conditions Article References: Liang, X.
Physics-embedded latent hydrogen energy flow learning under sparse sensing conditions.