北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (4): 29-35,43.

• 体系化人工智能专题 • 上一篇    下一篇

基于物理信息变分自编码器的计算流体动力学修复

王佳敏1, 颜哲熙1, 王笑琨1,2,3, 张雅斓1,2, 郭 宇4   

  1. 1. 北京科技大学 智能科学与技术学院; 2. 北京科技大学 顺德创新学院; 3. 北京科技大学 北京材料基因工程高精尖创新中心; 4. 北京科技大学 计算机与通信工程学院
  • 收稿日期:2023-12-31 修回日期:2024-03-29 出版日期:2024-08-28 发布日期:2024-08-26
  • 通讯作者: 王笑琨 E-mail:wangxiaokun@ustb.edu.cn
  • 基金资助:
    国家自然科学基金; 广东省基础与应用基础研究基金项目

Inpainting Computational Fluid Dynamics with Physics-Informed Variational Autoencoder

WANG Jiamin1, YAN Zhexi1, WANG Xiaokun1,2,3, ZHANG Yalan1,2, GUO Yu4   

  • Received:2023-12-31 Revised:2024-03-29 Online:2024-08-28 Published:2024-08-26

摘要: 为修复噪声干扰或局部缺失的流体流动数据,实现精确的流体动力学分析,提出了一种基于物理信息的变分自编码器模型。首先,利用变分自编码器学习流体流动的潜在表示;其次,将时空坐标信息与流体流动的潜在表示结合,通过自动微分技术获得解码后的流场信息关于时空坐标输入的偏导数;最后,引入流体动力学的物理先验信息,构造物理约束损失项,使得生成的数据同时符合流动关键特征和底层物理定律,从而增强了流体流动的物理一致性和重建精度,并且提供了一定的可解释性。实验结果表明,所提模型在处理流场噪声和数据缺失问题方面比现有方法具有更高的精度,并且在二维和三维复杂涡旋流场中都证明了其有效性。

关键词: 人工智能, 计算流体动力学, 物理先验, 数据去噪, 流场重建

Abstract:

To repair noisy or partially missing fluid flow data and achieve accurate fluid dynamics
analysis, a physics-informed variational autoencoder model is proposed. First, the variational autoencoder is employed to learn the latent representation of fluid flow. Second, spatiotemporal coordinate information is combined with this latent representation, and the partial derivatives of the decoded flow field information with respect to the spatiotemporal coordinates are obtained by automatic differentiation technology. Finally, physical prior information of fluid dynamics is introduced to construct a physical constraint loss term. This ensures that the generated data conforms to both the key features of the flow and the underlying physical laws, thereby enhancing the physical consistency and reconstruction accuracy while providing a certain level of interpretability. Experimental results demonstrate that the proposed model achieves higher accuracy than existing methods in dealing with flow field noise and data missing issues. Its effectiveness is also proven in both two-dimensional and three-dimensional complex vortex flow fields.

Key words: artificial intelligence,  computational fluid dynamics, physical priors, data denoising, flow field reconstruction

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