Physics Informed Neural Networks for Fluid Flow


  • 开始时间:2021-12-16 11:20
  • 活动地点:腾讯会议:187-850-699
  • 主讲人:王志成


omographic background oriented Schlieren (Tomo-BOS) imaging measures density or temperature fields in three dimensions using multiple camera BOS projections, and is particularly useful for instantaneous flow visualizations of complex fluid dynamics problems. We propose a new method based on physics-informed neural networks (PINNs) to infer the full continuous three-dimensional (3-D) velocity and pressure fields from snapshots of 3-D temperature fields obtained by Tomo-BOS imaging. The PINNs seamlessly integrate the underlying physics of the observed fluid flow and the visualization data, hence enabling the inference of latent quantities using limited experimental data. In this hidden fluid mechanics paradigm, we train the neural network by minimizing a loss function composed of a data mismatch term and residual terms associated with the coupled Navier–Stokes and heat transfer equations. We first quantify the accuracy of the proposed method based on a two-dimensional synthetic data set for buoyancy-driven flow, and subsequently apply it to the Tomo-BOS data set, where we are able to infer the instantaneous velocity and pressure fields of the flow over an espresso cup based only on the temperature field provided by the Tomo-BOS imaging. Moreover, we conduct an independent PIV exper


王志成,博士 2007 年获得北京交通大学热能与动力工程学士学位,2013 年在唐大伟研究员的指导下获得中国科学院工程热物理研究所博士学位并留所工作,2016 年至2021年先后在美国麻省理工学院机械工程系和布朗大学应用数学系从事博士后研究,与 M.T. Triantafyllou, G.E. Karniadakis 等教授合作,2021年5月进入大连理工能源与动力学院任副教授。成果发表在 J. Fluid Mechanics, J. Computational Physics, PNAS, CMAME 等期刊。王博士的主要研究兴趣包括湍流和两相流数值计算,谱元法,机器学习等。