Error prediction in electromagnetic simulations using machine learning


KARAOSMANOGLU B., ERGÜL Ö. S.

2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019, Georgia, Amerika Birleşik Devletleri, 7 - 12 Temmuz 2019, ss.787-788 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/apusncursinrsm.2019.8889056
  • Basıldığı Şehir: Georgia
  • Basıldığı Ülke: Amerika Birleşik Devletleri
  • Sayfa Sayıları: ss.787-788
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

© 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.