ExplainabilityTransparencyTrustworthinesscs.LG
Explainable Machine Learning: An Illustration of Kolmogorov-Arnold Network Model for Airfoil Lift Prediction
Sudhanva Kulkarni
January 29, 2025
arXiv.org
Key Takeaways
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Explainability and accuracy can coexist: The Kolmogorov-Arnold Network (KAN) achieved 96.17% accuracy on airfoil lift prediction while remaining interpretable, challenging the common assumption that the most accurate models must be "black boxes" that cannot explain their reasoning.
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Explainable AI can validate scientific knowledge: When KAN's predictions were converted into mathematical equations, the results aligned with established physics principles of lift generation, suggesting that explainable models can serve as tools for confirming and discovering scientific principles rather than just making predictions.
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Transparency enables new scientific discovery: By making machine learning models interpretable, researchers can extract actionable insights and equations from data rather than simply accepting predictions, potentially accelerating scientific exploration across fields beyond aerospace engineering.
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Model interpretability matters for responsible deployment: The paper illustrates why transparency in AI systems is important—when models can explain their reasoning and be validated against known scientific principles, they become more trustworthy for real-world applications where understanding the "why" behind predictions is critical.
Abstract
Data science has emerged as fourth paradigm of scientific exploration. However many machine learning models operate as black boxes offering limited insight into the reasoning behind their predictions. This lack of transparency is one of the drawbacks to generate new knowledge from data. Recently Kolmogorov-Arnold Network or KAN has been proposed as an alternative model which embeds explainable AI. This study demonstrates the potential of KAN for new scientific exploration. KAN along with five other popular supervised machine learning models are applied to the well-known problem of airfoil lift prediction in aerospace engineering. Standard data generated from an earlier study on 2900 different airfoils is used. KAN performed the best with an R2 score of 96.17 percent on the test data, surpassing both the baseline model and Multi Layer Perceptron. Explainability of KAN is shown by pruning and symbolizing the model resulting in an equation for coefficient of lift in terms of input variables. The explainable information retrieved from KAN model is found to be consistent with the known physics of lift generation by airfoil thus demonstrating its potential to aid in scientific exploration.