<ul data-eligibleForWebStory="false"><li>Magnetotelluric forward modeling is important for enhancing the accuracy and efficiency of MT inversion by solving related partial differential equations.</li><li>Neural operators (NOs) have been effective in quick MT forward modeling but often use multi-layer perceptrons (MLPs), which may reduce accuracy due to their drawbacks like lack of interpretability and overfitting.</li><li>A new neural operator called EFKAN combines Fourier neural operator (FNO) with Kolmogorov-Arnold network to improve MT forward modeling accuracy and explore alternatives to MLPs.</li><li>Experimental results show that EFKAN achieves higher accuracy in obtaining resistivity and phase compared to NOs with MLPs and is faster than traditional numerical methods.</li></ul>

EFKAN: A KAN-Integrated Neural Operator For Efficient Magnetotelluric Forward Modeling

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