Solution of an Inverse Problem of Optical Spectroscopy Using Kolmogorov-Arnold Networks

Abstract

Kolmogorov-Arnold Networks (KAN), introduced in May 2024, are a novel type of artificial neural networks, whose abilities and properties are now being actively investigated by the machine learning community. In this study, we test application of KAN to solve an inverse problem for development of multimodal carbon luminescent nanosensors of ions dissolved in water, including heavy metal cations. We compare the results of solving this problem with four various machine learning methods—random forest, gradient boosting over decision trees, multi-layer perceptron neural networks, and KAN. Advantages and disadvantages of KAN are discussed, and it is demonstrated that KAN has high chance to become one of the algorithms most recommended for use in solving highly non-linear regression problems with moderate number of input features.