Explainable Machine Learning: An Illustration of Kolmogorov-Arnold Network Model for Airfoil Lift Prediction
This work addresses the lack of transparency in machine learning for aerospace engineers, offering an incremental improvement in explainability for a specific domain.
The study tackled the problem of black-box machine learning models by applying the Kolmogorov-Arnold Network (KAN) to airfoil lift prediction, achieving an R2 score of 96.17% on test data, which outperformed baseline models and provided an explainable equation consistent with known physics.
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.