用自适应网格提升海洋预报效率，降低算力消耗62%以上
OceanLight: Efficient Global Ocean Forecasting via Geometry-Adaptive Unstructured Mesh Representation
- 采用几何自适应的不规则网格表示，动态匹配海洋复杂区域
- 在保持高精度的同时，显存减少62%，计算量降低70%
- 适合需要高效、精准海洋模拟的研究与实际应用
可靠的全球海洋预报对气候监测、海上航行和极端事件预警至关重要。基于物理的海洋模型计算成本过高，而现有深度学习方法多依赖规则网格结构，在陆地区域产生冗余计算，且在动态变化的海洋区域强制统一分辨率，无法适应局部流场复杂性。本文提出OceanLight，创新性地结合几何自适应不规则网格标记化与图神经网络（GNN）骨干网络。OceanLight在点位预测精度和动能谱保真度上超过操作型数值分析及最先进的基于AI的模型，且在地转平衡一致性方面优于所有基于AI的海洋模型。此外，该模型能可靠表征中尺度涡旋，捕捉超越点位统计优化的完整海洋结构。相比规则网格基线，其GPU内存消耗降低62%，浮点运算量（FLOPs）减少70%。该不规则网格表示为可扩展的数据驱动海洋学提供了通用范式。
原文摘要 · Abstract (English)
Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs, while existing deep learning approaches predominantly rely on structured-grid architectures, incurring unnecessary computation on masked land cells and enforcing uniform resolution across dynamically heterogeneous ocean regions regardless of local flow complexity. Here we present OceanLight, an efficient global ocean forecasting framework innovatively combining geometry-adaptive unstructured mesh tokenization with a graph neural network (GNN) backbone. OceanLight achieves pointwise forecast accuracy and kinetic energy spectral fidelity exceeding both operational numerical analyses and state-of-the-art AI-based models, while surpassing all AI-based ocean models in geostrophic balance consistency. Furthermore, OceanLight demonstrates reliable mesoscale eddy representation, capturing coherent ocean structures beyond pointwise statistical optimization. These capabilities are delivered with a 62% reduction in GPU memory consumption and 70\% reduction in FLOPs relative to structured-grid baselines. Our unstructured mesh representation establishes a generalizable paradigm for scalable data-driven oceanography.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。