Inferring particle distributions in two-dimensional space with numerical features based on generative adversarial networks (GANs)
A feasibility study was conducted on the usage of Generative Adversarial Networks (GANs) for inferring particle beam profiles. Two types of GANs, Deep Convolution GAN (DCGAN) and Wasserstein GAN (WGAN), were implemented in the PyTorch framework and trained using a mathematically generated dataset. T...
| 出版年: | Nuclear Engineering and Technology |
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| 第一著者: | |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
Elsevier
2025-10-01
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| 主題: | |
| オンライン･アクセス: | http://www.sciencedirect.com/science/article/pii/S1738573325002499 |