让图神经网络的预测结果能看清每个特征的空间影响
FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models
- 按特征独立分配空间重要性，突破传统GNN的混淆问题
- 在大气与流体模型中保持高精度并揭示物理相关模式
- 适合需要解释性的科学计算场景，如气候模拟、工程仿真
本文提出一种新型图神经网络架构——特征特定可解释图神经网络（FIGNN），用于提升科学计算中基于非结构化网格的深度学习代理模型的可解释性。传统GNN在多变量预测任务中常模糊不同特征的空间影响。FIGNN通过引入特征特定池化策略，实现对每个预测变量的空间重要性独立归因；同时在训练目标中加入基于掩码的正则项，显式促进可解释性与预测误差的一致性，推动模型性能的局部化归因。该方法在两个物理性质不同的系统上进行评估：SPEEDY大气环流模型和后向台阶（BFS）流体动力学基准。结果表明，FIGNN在保持竞争性预测性能的同时，揭示了每个特征独有的物理意义空间模式。通过对滚动预测稳定性、特征级误差预算及空间掩码叠加的分析，验证了FIGNN作为复杂物理领域可解释代理建模通用框架的有效性。
原文摘要 · Abstract (English)
This work presents a novel graph neural network (GNN) architecture, the Feature-specific Interpretable Graph Neural Network (FIGNN), designed to enhance the interpretability of deep learning surrogate models defined on unstructured grids in scientific applications. Traditional GNNs often obscure the distinct spatial influences of different features in multivariate prediction tasks. FIGNN addresses this limitation by introducing a feature-specific pooling strategy, which enables independent attribution of spatial importance for each predicted variable. Additionally, a mask-based regularization term is incorporated into the training objective to explicitly encourage alignment between interpretability and predictive error, promoting localized attribution of model performance. The method is evaluated for surrogate modeling of two physically distinct systems: the SPEEDY atmospheric circulation model and the backward-facing step (BFS) fluid dynamics benchmark. Results demonstrate that FIGNN achieves competitive predictive performance while revealing physically meaningful spatial patterns unique to each feature. Analysis of rollout stability, feature-wise error budgets, and spatial mask overlays confirm the utility of FIGNN as a general-purpose framework for interpretable surrogate modeling in complex physical domains.
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