Eleketromanyetik etkileşim karmaşıklığının derin öğrenim kullanarak düşürülmesi.

In this thesis, we present a novel approach to accelerate electromagnetic simulations by the multilevel fast multipole algorithm (MLFMA). The strategy is based on a progressive elimination of electromagnetic interactions, resulting in trimmed tree structures, during iterative solutions. To systematically perform such eliminations, artificial neural network (ANN) models are constructed and trained to estimate errors in updated surface current coefficients. These column eliminations are supported by straightforward row eliminations, leading to increasingly sparse tree structures and matrix equations as iterations continue. We show that the proposed implementation, namely trimmed MLFMA (T-MLFMA), leads to significantly accelerated electromagnetic simulations of large-scale objects, while the accuracy is still much better than the high-frequency techniques. T-MLFMA can be seen as an exemplar of implementations, where machine learning is successfully integrated into an electromagnetic solver for enhanced simulations.Thesis (Ph.D.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering