FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models

2025-06-13

AI Summary

Traditional graph neural networks (GNNs) struggle to disentangle the spatial influence of individual features in multi-variable forecasting on unstructured scientific meshes, severely limiting model interpretability. To address this, we propose FIGNN—a novel GNN architecture featuring (i) feature-specific pooling that isolates spatial dependencies per predicted variable, and (ii) an interpretable-error-aligned learnable spatial mask regularization that enforces physically consistent attribution of spatial importance. By incorporating physics-informed training objectives, FIGNN decouples feature representations while preserving predictive fidelity. Evaluated on the SPEEDY atmospheric model and BFS fluid dynamics benchmark, FIGNN achieves state-of-the-art prediction accuracy and uncovers physically plausible, feature-specific spatial patterns—e.g., distinct pressure- versus vorticity-driven flow structures. This advances scientific surrogate modeling by jointly improving both predictive performance and mechanistic interpretability.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

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.

Problem

• Enhances interpretability of GNN surrogate models for unstructured grids
• Addresses obscured spatial influences in multivariate prediction tasks
• Promotes localized attribution of model performance via regularization

Innovation

• Feature-specific pooling for independent spatial attribution
• Mask-based regularization aligns interpretability with error
• Competitive predictive performance with meaningful patterns