Lagrangian–Eulerian learning of flow field and trajectories with TrajectoryFlowNet
摘要
Wan 等人在 Nature Machine Intelligence 发表 TrajectoryFlowNet，这是一个物理信息学习模型，可基于非常有限的轨迹测量同时预测长程粒子路径和流场。研究在模拟和血流实验中对该模型进行了验证。doi:10.1038/s42256-026-01316-7
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正文 · 原文
Nature Machine Intelligence, Published online: 20 October 2026; doi:10.1038/s42256-026-01316-7
Wan et al. introduce a physics-informed learning model that simultaneously predicts long-range particle paths and flow fields based on very limited trajectory measurements, validated in both simulations and blood-flow experiments.
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来源：Nature Machine Intelligence · nature.com
引用原文
Jingdi Wan. Lagrangian–Eulerian learning of flow field and trajectories with TrajectoryFlowNet[EB/OL]. (2026-10-20)[访问日期]. https://www.nature.com/articles/s42256-026-01316-7.