A Machine Learning Model for Humidity Estimation Based on Physics-Informed Dimensionality Reduction

Accurate and efficient humidity estimation is critical for various applications, particularly with the rise of IoT devices and smart sensors where computational resources are limited. A common issue with many conventional humidity sensors is their slow response dynamics, which restricts their performance in applications requiring rapid, real-time data. This work introduces a novel Machine Learning algorithm for fast humidity estimation, based on analyzing the voltage discharge dynamics across microelectrodes, computationally frugal yet accurate estimation method suitable for resource-constrained environments. We propose a Physics-Informed Dimensionality Reduction (PIDR) methodology that leverages an underlying physical model – specifically, anomalous diffusion governing the electric discharge between microelectrodes – to extract low-dimensional, physically meaningful features from high-dimensional sensor time series data. Neural Parameter Estimation (NPE), trained effectively on synthetically augmented data guided by limited experimental observations, maps the voltage discharge curves to the anomalous diffusion parameters of the physical model. These parameters, representing a low dimensional physical space, are then fed alongside temperature readings into a compact Artificial Neural Network (ANN) for final humidity prediction. This two-stage, physics-aware architecture significantly reduces model complexity. Experimental results demonstrate the effectiveness of the PIDR approach, achieving high prediction accuracy while demanding significantly less computational effort and training data than traditional parameter estimation techniques or purely data-driven deep learning models applied to raw data. Our study highlights the successful integration of physical principles with machine learning for developing efficient, interpretable, and robust AI solutions tailored for smart sensors and sustainable IoT applications.