In this paper, we propose mesoscience-guided deep learning (MGDL), a deep learning modeling approach guided by mesoscience, to study complex systems. When establishing sample dataset based on the same system evolution data, different from the operation of conventional deep learning method, MGDL introduces the treatment of the dominant mechanisms of complex system and interactions between them according to the principle of compromise in competition (CIC) in mesoscience. Mesoscience constraints are then integrated into the loss function to guide the deep learning training. Two methods are proposed for the addition of mesoscience constraints. The physical interpretability of the model-training process is improved by MGDL because guidance and constraints based on physical principles are provided. MGDL was evaluated using a bubbling bed modeling case and compared with traditional techniques. With a much smaller training dataset, the results indicate that mesoscience-constraint-based model training has distinct advantages in terms of convergence stability and prediction accuracy, and it can be widely applied to various neural network configurations. The MGDL approach proposed in this paper is a novel method for utilizing the physical background information during deep learning model training. Further exploration of MGDL will be continued in the future.

中文翻译：

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将介科学应用于深度学习的案例研究

在本文中，我们提出了介科学引导的深度学习（MGDL），这是一种以介科学为指导的深度学习建模方法，用于研究复杂系统。在基于相同系统演化数据建立样本数据集时，与传统深度学习方法的操作不同，MGDL根据介科学中的竞争中妥协（CIC）原理引入了对复杂系统主导机制及其相互作用的处理。然后将介科学约束集成到损失函数中以指导深度学习训练。提出了两种添加介科学约束的方法。 MGDL 提高了模型训练过程的物理可解释性，因为提供了基于物理原理的指导和约束。使用鼓泡床建模案例评估 MGDL，并与传统技术进行比较。结果表明，在训练数据集较小的情况下，基于介科学约束的模型训练在收敛稳定性和预测精度方面具有明显的优势，并且可以广泛应用于各种神经网络配置。本文提出的 MGDL 方法是一种在深度学习模型训练过程中利用物理背景信息的新方法。未来将继续对MGDL进行进一步的探索。