Sensitivity Fidelity Reveals Physical Dependencies Beyond Forecast Accuracy in Deep-Learning Earth-System Models
The study introduces sensitivity fidelity as a measure to check if deep learning models learn correct physical rules, not just accurate predictions. By comparing model responses to small input changes against established physics, researchers found that some models, like IFactFormer-RB in convection simulations, learned the governing physical dynamics more faithfully than others.