用遮蔽图神经网络提升流体模拟精度，大幅改善长期预测效果。
MeshMask: Physics-Based Simulations with Masked Graph Neural Networks
- 随机遮蔽40%网格节点，强制模型学习鲁棒流体特征。
- 在7个流体数据集上达到顶尖性能，3D脑动脉瘤模拟超25万节点。
- 支持多数据集联合预训练，减少训练时间和数据需求。
我们提出一种面向计算流体动力学（CFD）问题的新型图神经网络遮蔽预训练方法。通过在预训练阶段随机遮蔽最多40%的输入网格节点，迫使模型学习复杂流体动力学的鲁棒表示。结合非对称编码器-解码器架构与门控多层感知机，进一步提升性能。该方法在七个CFD数据集上取得当前最优结果，包括一个包含超过25万个节点的3D脑动脉瘤模拟新数据集。相比此前最佳模型，长期预测准确率最高提升60%，且计算成本相近。此外，该方法可同时在多个数据集上进行有效预训练，显著降低新任务所需的时间与数据量。通过大量消融实验，揭示了最优遮蔽比例、架构选择与训练策略的内在机制。
原文摘要 · Abstract (English)
We introduce a novel masked pre-training technique for graph neural networks (GNNs) applied to computational fluid dynamics (CFD) problems. By randomly masking up to 40\% of input mesh nodes during pre-training, we force the model to learn robust representations of complex fluid dynamics. We pair this masking strategy with an asymmetric encoder-decoder architecture and gated multi-layer perceptrons to further enhance performance. The proposed method achieves state-of-the-art results on seven CFD datasets, including a new challenging dataset of 3D intracranial aneurysm simulations with over 250,000 nodes per mesh. Moreover, it significantly improves model performance and training efficiency across such diverse range of fluid simulation tasks. We demonstrate improvements of up to 60\% in long-term prediction accuracy compared to previous best models, while maintaining similar computational costs. Notably, our approach enables effective pre-training on multiple datasets simultaneously, significantly reducing the time and data required to achieve high performance on new tasks. Through extensive ablation studies, we provide insights into the optimal masking ratio, architectural choices, and training strategies.
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