Interpretable Fine-Tuning for Graph Neural Network Surrogate Models: Enhancing Predictive Capabilities with Explainable Links

The author introduces an interpretable fine-tuning strategy for Graph Neural Networks (GNNs) to enhance predictive capabilities and provide explainable links between the model architecture, optimization goal, and known physics.