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...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-10-01
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| Series: | Nuclear Engineering and Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1738573325002499 |
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