使用VAE-GAN混合模型和区分器驱动的潜在采样进行拓磁性结构生成
S M Park1, H G Yoon1, D B Lee1,2
1Department of Physics, Kyung Hee University, Seoul, 02447, South Korea.
Scientific reports
|November 22, 2023
概括
本研究介绍了一种混合变异自编码器 (VAE) 和生成对抗网络 (GAN) 模型,以生成多样化和可信的二维磁拓结构. 该模型有效地导航复杂的能源景观,以创建有效的旋转结构.
科学领域:
- 材料科学 材料科学 材料科学
- 计算物理 计算物理
- 机器学习 机器学习
背景情况:
- 深度生成模型越来越多地用于科学数据生成.
- 由于能量障碍,生成多样化的磁拓结构具有挑战性.
- 现有的方法难以产生多样化和有效的旋转结构.
研究的目的:
- 开发一种混合变异自编码器 (VAE) 和生成对抗网络 (GAN) 模型,用于生成二维磁拓结构.
- 为了利用VAE (多样性) 和GAN (忠实性) 的优势来生成科学数据.
- 探索区分器驱动潜伏采样 (DDLS) 的应用,以提高生成样本的质量.
主要方法:
- 实施混合VAE-GAN模型.
- 使用模型生成二维磁拓结构数据.
- 采用区分器驱动的隐性采样 (DDLS) 来改进生成的样本.
主要成果:
- VAE-GAN混合模型成功生成了各种可信的2D磁拓结构.
- 该模型展示了克服能源和拓障碍的能力.
- 区分驱动的隐性采样 (DDLS) 提高了生成数据的质量和覆盖范围,遵守拓规则.
结论:
- VAE-GAN混合模型为生成多样化和有效的磁拓结构提供了一种有效的方法.
- 该方法促进了诸如在大量有效结构中搜索所需样本等应用.
- DDLS是一种有价值的技术,可以提高生成的科学数据的准确性和覆盖范围.
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