Error prediction in electromagnetic simulations using machine learning

Date

2019-07-01

Author

KARAOSMANOGLU, BARISCAN
Ergül, Özgür Salih

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© 2019 IEEE.We present a novel approach of using deep convolutional neural networks (CNN) to predict electromagnetic scattering errors in iterative solutions of electrically large three-dimensional objects. Deep CNN models are constructed and trained by using surface current images to predict far-zone scattering errors. Numerical experiments demonstrate successful predictions with more than 95% accuracy. The constructed models can be useful to quickly assess the accuracy of candidate solutions of current distributions via their images.

Subject Keywords

Training, Electromagnetics, Scattering, Convolution, Task analysis, Predictive models , Machine learning

URI

https://hdl.handle.net/11511/56764

DOI

https://doi.org/10.1109/apusncursinrsm.2019.8889056

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Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

B. KARAOSMANOGLU and Ö. S. Ergül, “Error prediction in electromagnetic simulations using machine learning,” 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/56764.