FEM-GAN:用于弹性多孔材料的物理监督深度学习生成模型
1MOE Key Laboratory of Soft Soils and Geoenvironmental Engineering, Zhejiang University, Hangzhou 310058, China.
Materials (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了一种物理监督的生成对抗网络 (GAN),用于创建X射线微计算断层扫描 (μCT) 图像. 新模型产生了现实的图像,改善了材料科学中的数据增强和分析.
科学领域:
- 材料科学 材料科学 材料科学
- 计算成像技术的成像
- 人工智能的人工智能
背景情况:
- 射线微型计算机断层扫描 (μCT) 对于材料表征至关重要,但成本昂贵且耗时.
- 现有的生成模型往往缺乏物理基础,限制了它们在科学成像中的应用.
- 需要物理信息生成模型来提高X射线μCT图像的可访问性.
研究的目的:
- 开发和评估一个由物理监督的生成对抗网络 (GAN),用于生成X射线μCT图像.
- 在GAN培训过程中整合有限元法 (FEM) 模拟用于物理指导.
- 评估生成的X射线μCT图像的质量和实用性,用于各种应用.
主要方法:
- 开发了一个由物理监督的生成对抗网络 (GAN),结合FEM模拟的弹性系数.
- 用Hostun沙X射线μCT图像作为目标数据.
- 使用非参数统计数据在培训期间进行后期度量评估.
- 一项参数研究调查了各种损失函数和FEM评估频率.
主要成果:
- 对于大多数弹性系数,FEM-GANs模型产生了X射线μCT图像,其性能优于参考图像.
- 虽然该模型无法完美地复制所有系数,但该模型比标准GAN显著改善.
- 生成的图像显示了改进数据增强和图像分析任务的潜力.
结论:
- 物理监督的GAN为生成现实的X射线μCT图像提供了一种可行的方法.
- 这种方法可以减轻与传统的X射线μCT成像相关的局限性,例如成本和时间.
- 生成的图像在数据增强,工具校准和多尺度建模方面具有实际应用.
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