Adaptive Physics-Guided Neural Network

TL;DR

This paper presents an adaptive physics-guided neural network that integrates physical laws into deep learning models, improving accuracy and robustness in predicting image-based quality attributes across synthetic and real-world datasets.

Contribution

The proposed APGNN adaptively balances physics-informed and data-driven predictions, enhancing model performance in diverse environments compared to traditional models.

Findings

APGNN outperforms ResNet in complex thermal datasets.

Adaptive balancing improves robustness across varied conditions.

Synthetic data experiments validate physical law integration.

Abstract

This paper introduces an adaptive physics-guided neural network (APGNN) framework for predicting quality attributes from image data by integrating physical laws into deep learning models. The APGNN adaptively balances data-driven and physics-informed predictions, enhancing model accuracy and robustness across different environments. Our approach is evaluated on both synthetic and real-world datasets, with comparisons to conventional data-driven models such as ResNet. For the synthetic data, 2D domains were generated using three distinct governing equations: the diffusion equation, the advection-diffusion equation, and the Poisson equation. Non-linear transformations were applied to these domains to emulate complex physical processes in image form. In real-world experiments, the APGNN consistently demonstrated superior performance in the diverse thermal image dataset. On the cucumber dataset, characterized by low material diversity and controlled conditions, APGNN and PGNN showed similar performance, both outperforming the data-driven ResNet. However, in the more complex thermal dataset, particularly for outdoor materials with higher environmental variability, APGNN outperformed both PGNN and ResNet by dynamically adjusting its reliance on physics-based versus data-driven insights. This adaptability allowed APGNN to maintain robust performance across structured, low-variability settings and more heterogeneous scenarios. These findings underscore the potential of adaptive physics-guided learning to integrate physical constraints effectively, even in challenging real-world contexts with diverse environmental conditions.