Data-driven simulation in fluids animation: A survey

被引：0
作者：
Chen Q. [1 ]
Wang Y. [1 ]
Wang H. [1 ]
Yang X. [1 ]
机构：
[1] School of Software, Shanghai Jiao Tong University, Shanghai
来源：
Virtual Reality and Intelligent Hardware | 2021年 / 3卷 / 02期
关键词：
Data driven; Fluid simulation; Machine learning;
D O I：
10.1016/j.vrih.2021.02.002
中图分类号：
学科分类号：
摘要：
The field of fluid simulation is developing rapidly, and data-driven methods provide many frameworks and techniques for fluid simulation. This paper presents a survey of data-driven methods used in fluid simulation in computer graphics in recent years. First, we provide a brief introduction of physicalbased fluid simulation methods based on their spatial discretization, including Lagrangian, Eulerian, and hybrid methods. The characteristics of these underlying structures and their inherent connection with datadriven methodologies are then analyzed. Subsequently, we review studies pertaining to a wide range of applications, including data-driven solvers, detail enhancement, animation synthesis, fluid control, and differentiable simulation. Finally, we discuss some related issues and potential directions in data-driven fluid simulation. We conclude that the fluid simulation combined with data-driven methods has some advantages, such as higher simulation efficiency, rich details and different pattern styles, compared with traditional methods under the same parameters. It can be seen that the data-driven fluid simulation is feasible and has broad prospects. © 2020 Beijing Zhongke Journal Publishing Co. Ltd
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页码：87 / 104
页数：17
相关论文
共 121 条
  • [1] Fei F., Zhang J., Li J., Liu Z.H., A unified stochastic particle Bhatnagar-Gross-Krook method for multiscale gas flows, Journal of Computational Physics, 400, (2020)
  • [2] Zhang J., Tian P., Yao S., Fei F., Multiscale investigation of Kolmogorov flow: From microscopic molecular motions to macroscopic coherent structures, Physics of Fluids, 31, 8, (2019)
  • [3] Zhang J., John B., Pfeiffer M., Fei F., Wen D.S., Particle-based hybrid and multiscale methods for nonequilibrium gas flows, Advances in Aerodynamics, 1, 1, pp. 1-15, (2019)
  • [4] Zhang J., Onskog T., Langevin equation elucidates the mechanism of the Rayleigh-Bénard instability by coupling molecular motions and macroscopic fluctuations, Physical Review. E, 96, 4, (2017)
  • [5] Losasso F., Gibou F., Fedkiw R., (2004)
  • [6] Berger M.J., Colella P., Local adaptive mesh refinement for shock hydrodynamics, Journal of Computational Physics, 82, 1, pp. 64-84, (1989)
  • [7] Yan H., Wang Z.Y., He J., Chen X., Wang C.B., Peng Q.S., Real-time fluid simulation with adaptive SPH, Computer Animation and Virtual Worlds, 20, 2-3, pp. 417-426, (2009)
  • [8] Ferstl F., Ando R., Wojtan C., Westermann R., Thuerey N., Narrow band FLIP for liquid simulations, Computer Graphics Forum, 35, 2, pp. 225-232, (2016)
  • [9] Chu J.Y., Zafar N.B., Yang X.B., A schur complement preconditioner for scalable parallel fluid simulation, ACM Transactions on Graphics, 36, 4, (2017)
  • [10] Gao M., Wang X.L., Wu K., Pradhana A., Sifakis E., Yuksel C., Jiang C., GPU optimization of material point methods, ACM Transactions on Graphics, 37, 6, pp. 1-12, (2019)
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