A Physics-Informed GAN Framework for Modeling Spatiotemporal Correlated Temperature Field During Grain Storage

Accurate modeling of the spatiotemporal temperature field in grain storage is crucial for maintaining grain quality and optimizing storage management. However, existing methods struggle to accurately capture the stochastic variations and high-dimensional correlation in spatiotemporal dynamics, thereby limiting their effectiveness in achieving higher predictive accuracy in real-world applications. This paper proposes an improved Physics-Informed Generative Adversarial Network (PI-GAN) framework for modeling complex spatiotemporal dynamics while quantifying stochastic uncertainties. Stochastic latent variables are introduced to capture high-dimensional uncertainties, while physics constraints are embedded into both the generator and discriminator to fully exploit adversarial learning. By integrating PDE constraints into the GAN structure, the proposed method enhances generalization performance and achieves higher predictive accuracy. To validate its effectiveness, the framework is applied to a grain storage case study, modeling the temperature field within a granary. Model results demonstrate that the proposed approach outperforms state-of-the-art methods in terms of predictive accuracy and robustness.