用可解释的KAN模型预测机翼升力，效果优于传统模型。
Explainable Machine Learning: An Illustration of Kolmogorov-Arnold Network Model for Airfoil Lift Prediction
- 采用KAN替代黑箱模型，嵌入可解释性设计
- 测试集R2达96.17%，优于MLP等五种模型
- 提取出符合物理规律的升力公式，适合科研探索
数据科学已成为第四范式的研究方法。然而，许多机器学习模型如同黑箱，难以揭示其预测背后的逻辑，制约了从数据中生成新知识。最近提出的科尔莫戈罗夫-阿诺德网络（Kolmogorov-Arnold Network, KAN）提供了一种可解释的人工智能路径。本研究展示了KAN在科学探索中的潜力：将KAN与另外五种主流监督学习模型应用于航空航天工程中的经典问题——机翼升力预测。使用先前研究生成的标准数据集，涵盖2900个不同机翼形状。KAN在测试数据上取得96.17%的R²得分，超越基线模型和多层感知机（MLP）。通过剪枝与符号化，成功提取出升力系数关于输入变量的显式表达式。该表达式与已知的升力生成物理规律一致，证明KAN能辅助科学发现。
原文摘要 · Abstract (English)
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.
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