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.

翻译：本文提出了一种自适应物理引导神经网络（APGNN）框架，通过将物理定律整合到深度学习模型中，从图像数据预测质量属性。APGNN自适应地平衡数据驱动与物理信息预测，从而提升了模型在不同环境下的准确性与鲁棒性。我们的方法在合成数据集和真实世界数据集上进行了评估，并与ResNet等传统数据驱动模型进行了比较。对于合成数据，我们使用三个不同的控制方程生成了二维域：扩散方程、对流-扩散方程和泊松方程。对这些域施加非线性变换，以模拟图像形式的复杂物理过程。在真实世界实验中，APGNN在多样化的热图像数据集上始终表现出优越性能。在材料多样性低、条件受控的黄瓜数据集上，APGNN与PGNN表现出相近的性能，两者均优于数据驱动的ResNet。然而，在更复杂的热数据集中，特别是对于环境变异性较高的户外材料，APGNN通过动态调整其对物理基础洞察与数据驱动洞察的依赖，表现优于PGNN和ResNet。这种自适应性使APGNN能够在结构化、低变异性的场景以及更异质的场景中保持稳健的性能。这些发现强调了自适应物理引导学习在有效整合物理约束方面的潜力，即使在具有多样化环境条件的挑战性现实场景中亦是如此。