DeepLag：发现深度拉格朗日动力学以实现直观的流体预测
DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
AI总结：
针对欧拉视角下流体动力学难以预测的问题，提出DeepLag方法，通过追踪自适应采样关键粒子发现隐藏拉格朗日动力学，结合拉格朗日与欧拉信息实现高效可解释的流体预测，在多类任务中表现优异。
摘要：
准确预测未来流体状态对气象学、海洋学和空气动力学等诸多领域至关重要。然而，由于流体通常从欧拉视角观测，其运动的复杂动力学在静态网格中被严重遮蔽和混淆，给预测带来了棘手挑战。本文提出一种新的拉格朗日-欧拉结合范式来解决复杂的流体动力学问题。我们提出的DeepLag并非仅基于欧拉观测预测未来，而是通过追踪自适应采样的关键粒子的运动，发现流体中隐藏的拉格朗日动力学。此外，DeepLag提出了一种新的流体预测范式：从欧拉观测中推断被追踪粒子的拉格朗日运动，并将其累积的拉格朗日动力学信息分别融入全局欧拉演化特征，以指导未来预测。追踪关键粒子不仅为流体动力学提供了透明且可解释的线索，还使我们的模型无需对大量网格间的复杂相关性建模，从而提升了效率。实验中，DeepLag在涵盖2D与3D、模拟与真实流体的三项具有挑战性的流体预测任务中表现优异。代码可在该仓库获取：https://github.com/thuml/DeepLag。
英文摘要：
Accurately predicting the future fluid is vital to extensive areas such as meteorology, oceanology, and aerodynamics. However, since the fluid is usually observed from the Eulerian perspective, its moving and intricate dynamics are seriously obscured and confounded in static grids, bringing thorny challenges to the prediction. This paper introduces a new Lagrangian-Eulerian combined paradigm to tackle the tanglesome fluid dynamics. Instead of solely predicting the future based on Eulerian observations, we propose DeepLag to discover hidden Lagrangian dynamics within the fluid by tracking the movements of adaptively sampled key particles. Further, DeepLag presents a new paradigm for fluid prediction, where the Lagrangian movement of the tracked particles is inferred from Eulerian observations, and their accumulated Lagrangian dynamics information is incorporated into global Eulerian evolving features to guide future prediction respectively. Tracking key particles not only provides a transparent and interpretable clue for fluid dynamics but also makes our model free from modeling complex correlations among massive grids for better efficiency. Experimentally, DeepLag excels in three challenging fluid prediction tasks covering 2D and 3D, simulated and real-world fluids. Code is available at this repository: https://github.com/thuml/DeepLag.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
