用物理方程增强神经网络，提升流体预测长期准确性。
PINP: Physics-Informed Neural Predictor with latent estimation of fluid flows
- 将物理方程嵌入模型结构与损失函数，联合建模多物理量耦合
- 在数值模拟与极端降水预报中均达当前最佳性能
- 适合需要高精度长期流体预测的研究者与工程应用
准确预测流体动力学演化是物理科学中的长期挑战。传统深度学习方法通常依赖神经网络的非线性建模能力建立历史与未来状态间的映射，却忽视了流体动力学本质，或仅建模速度场，忽略了多物理量之间的耦合关系。本文提出一种新型物理信息学习方法，将耦合的物理量引入预测过程以辅助预报。方法核心在于对物理方程进行离散化，并直接嵌入模型架构与损失函数。该设计使模型具备鲁棒的长期未来预测能力，并展现出时间外推与空间泛化能力。实验结果表明，该方法在数值模拟与真实世界极端降水短临预报基准上均达到当前最优性能。
原文摘要 · Abstract (English)
Accurately predicting fluid dynamics and evolution has been a long-standing challenge in physical sciences. Conventional deep learning methods often rely on the nonlinear modeling capabilities of neural networks to establish mappings between past and future states, overlooking the fluid dynamics, or only modeling the velocity field, neglecting the coupling of multiple physical quantities. In this paper, we propose a new physics-informed learning approach that incorporates coupled physical quantities into the prediction process to assist with forecasting. Central to our method lies in the discretization of physical equations, which are directly integrated into the model architecture and loss function. This integration enables the model to provide robust, long-term future predictions. By incorporating physical equations, our model demonstrates temporal extrapolation and spatial generalization capabilities. Experimental results show that our approach achieves the state-of-the-art performance in spatiotemporal prediction across both numerical simulations and real-world extreme-precipitation nowcasting benchmarks.
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