隐藏的流体力学:从流动可视化中学习速度和压力场
Maziar Raissi1,2, Alireza Yazdani3, George Em Karniadakis1
1Division of Applied Mathematics, Brown University, Providence, RI 02906, USA. maziar.raissi@colorado.edu george_karniadakis@brown.edu.
概括
隐藏的流体力学 (HFM) 使用基于物理的深度学习来从图像中提取流体速度和压力,即使有噪音. 这种新的框架解决了复杂的流体动力学问题,
科学领域:
- 流体动力学
- 计算物理
- 生物医学工程
背景情况:
- 流动可视化在历史上有助于研究物理和生物系统中的流体运动.
- 纳维尔-斯托克斯方程理论上描述了流体流动,但从视觉观测中提取速度和压力等定量数据仍然具有挑战性.
- 在许多场景中,直接测量流体动力学可能是困难或不可能的.
研究的目的:
- 开发一个新的基于物理的深度学习框架,隐藏流体力学 (HFM),以克服从观测中提取定量流体动力学数据的局限性.
- 创建一个多功能框架,编码纳维埃-斯托克斯方程,使各种几何和条件的分析.
- 展示HFM在从物理和生物医学系统中提取其他难以获得的定量信息的实际应用.
主要方法:
- 开发了一种基于物理的深度学习框架 - - 隐藏流体力学 (HFM).
- 将纳维尔-斯托克斯方程直接集成到神经网络架构中.
- 设计HFM不受特定的几何形状,初始和边界条件的影响,从而具有广泛的适用性.
主要成果:
- 在各种物理和生物医学流量问题上成功展示了HFM.
- 从流动可视化数据中提取定量速度和压力场.
- 在观测数据中展示了HFM对低分辨率图像和显著噪声的稳定性.
结论:
- HFM提供了一种强大,数据驱动的方法来定量分析流体运动.
- 该框架能够处理噪音和低分辨率数据,为流体动力学研究和应用开辟了新的可能性.
- 在流体力学中,HFM提供了显著的进步,特别是对于直接测量不切实际的场景.
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