%0 Conference Paper %1 8277017 %A Tang, Wei %A Shan, Tao %A Dang, Xunwang %A Li, Maokun %A Yang, Fan %A Xu, Shenheng %A Wu, Ji %B 2017 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS) %D 2017 %K project:magnet todo:read %P 1-3 %R 10.1109/EDAPS.2017.8277017 %T Study on a Poisson's equation solver based on deep learning technique %U https://ieeexplore.ieee.org/document/8277017 %X In this work, we investigated the feasibility of applying deep learning techniques to solve 2D Poisson's equation. A deep convolutional neural network is set up to predict the distribution of electric potential in 2D. With training data generated from a finite difference solver, the strong approximation capability of the deep convolutional neural network allows it to make correct prediction given information of the source and distribution of permittivity. Numerical experiments show that the predication error can reach below one percent, with a significant reduction in CPU time compared with the traditional solver based on finite difference methods.

@inproceedings{8277017, abstract = {In this work, we investigated the feasibility of applying deep learning techniques to solve 2D Poisson's equation. A deep convolutional neural network is set up to predict the distribution of electric potential in 2D. With training data generated from a finite difference solver, the strong approximation capability of the deep convolutional neural network allows it to make correct prediction given information of the source and distribution of permittivity. Numerical experiments show that the predication error can reach below one percent, with a significant reduction in CPU time compared with the traditional solver based on finite difference methods.}, added-at = {2021-12-17T08:28:32.000+0100}, author = {Tang, Wei and Shan, Tao and Dang, Xunwang and Li, Maokun and Yang, Fan and Xu, Shenheng and Wu, Ji}, biburl = {https://www.bibsonomy.org/bibtex/201c2fa40a183ab7cfbe1208b8620dd85/annakrause}, booktitle = {2017 IEEE Electrical Design of Advanced Packaging and Systems Symposium (EDAPS)}, description = {Study on a Poisson's equation solver based on deep learning technique | IEEE Conference Publication | IEEE Xplore}, doi = {10.1109/EDAPS.2017.8277017}, interhash = {cd50cd4d5a6980d47a8bdec66481e8c3}, intrahash = {01c2fa40a183ab7cfbe1208b8620dd85}, issn = {2151-1233}, keywords = {project:magnet todo:read}, month = dec, pages = {1-3}, timestamp = {2021-12-17T08:28:32.000+0100}, title = {Study on a Poisson's equation solver based on deep learning technique}, url = {https://ieeexplore.ieee.org/document/8277017}, year = 2017 }