用偏微分方程基础模型模拟火星大气，仅需少量数据和算力就实现高精度预测。
PDE foundation models are skillful AI weather emulators for the Martian atmosphere
- 基于二维偏微分方程模型扩展至三维，保留预训练知识。
- 在稀疏初始条件下仍保持34.4%性能提升，验证泛化能力。
- 适合数据稀缺或算力有限的复杂气候建模任务。
我们证明，经过多样偏微分方程数值解预训练的AI基础模型，可通过微调成为火星大气的高效天气预测模型。研究基于二维系统模型Poseidon，提出方法将其扩展至三维，同时保留预训练信息。在四火星年（约34 GB）训练数据与13 GPU小时中等算力条件下，模型在未参与训练的年份上性能提升34.4%。结果表明，PDE基础模型不仅能近似求解其他偏微分方程，还能为缺乏足够数据或算力的真实复杂系统建模提供可靠锚点。
原文摘要 · Abstract (English)
We show that AI foundation models that are pretrained on numerical solutions to a diverse corpus of partial differential equations can be adapted and fine-tuned to obtain skillful predictive weather emulators for the Martian atmosphere. We base our work on the Poseidon PDE foundation model for two-dimensional systems. We develop a method to extend Poseidon from two to three dimensions while keeping the pretraining information. Moreover, we investigate the performance of the model in the presence of sparse initial conditions. Our results make use of four Martian years (approx.~34 GB) of training data and a median compute budget of 13 GPU hours. We find that the combination of pretraining and model extension yields a performance increase of 34.4\% on a held-out year. This shows that PDEs-FMs can not only approximate solutions to (other) PDEs but also anchor models for real-world problems with complex interactions that lack a sufficient amount of training data or a suitable compute budget.
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