Visual Result Prediction in Electromagnetic Simulations Using Machine Learning

Date

2019-11-01

Author

Karaosmanoglu, Bariscan
Ergül, Özgür Salih

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this letter, we present a novel approach based on using convolutional neural networks (CNNs) to visually predict solutions of electromagnetic problems. CNN models are constructed and trained such that images of surface currents obtained at the early stages of an iterative solution can be used to predict images of the final (converged) solution. Numerical experiments demonstrate that the predicted images contain significantly better visual details than the corresponding input images. The developed approach and the constructed CNN models can provide visual information on the solution of a given problem using only a few iterations without performing the whole iterative solution.

Subject Keywords

Electrical and Electronic Engineering

URI

https://hdl.handle.net/11511/37759

Journal

IEEE Antennas and Wireless Propagation Letters

DOI

https://doi.org/10.1109/lawp.2019.2939762

Collections

Department of Electrical and Electronics Engineering, Article

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Citation Formats

B. Karaosmanoglu and Ö. S. Ergül, “Visual Result Prediction in Electromagnetic Simulations Using Machine Learning,” IEEE Antennas and Wireless Propagation Letters, pp. 2264–2266, 2019, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/37759.