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肖祥云  等:基于物理及数据驱动的流体动画研究                                                          3265


        [105]     Bonev B, Prantl L, Thuerey N. Pre-computed liquid spaces with generative neural networks and optical flow. arXiv Preprint arXiv:
             1704.07854, 2017.
        [106]     Um K, Hu X, Thuerey N. Liquid splash modeling with neural networks. arXiv Preprint arXiv: 1704.04456, 2017.
        [107]     Ma P, Tian Y, Pan Z, et al. Fluid directed rigid body control using deep reinforcement learning. ACM Trans. on Graphics (TOG),
             2018,37(4):96:1−96:11.
        [108]     Xiao XY. Reserch on deep neural network based fluid simulation [Ph.D. Thesis]. Shanghai: Shanghai Jiaotong University, 2019 (in
             Chinese with English abstract).
        [109]    Foster  N, Fedkiw  R. Practical  animation of liquids. In: Proc. of  the 28th  Annual Conf. on  Computer Graphics  and Interactive
             Techniques (SIGGRAPH 2001). 2001. 23−30.
        [110]     Katrutsa A, Daulbaev T,  Oseledets I. Deep multigrid: Learning  prolongation and restriction matrices. arXiv  Preprint arXiv:
             1711.03825, 2017.
        [111]     Abadi M, Barham P, Chen J, et al. TensorFlow: A system for large-scale machine learning TensorFlow: A system for large-scale
             machine  learning.  In: Proc. of  the 12th USENIX Symp. on Operating Systems  Design  and Implementation (OSDI 2016). 2016.
             265−284.
        [112]     Abadi M, Agarwal  A, Barham  P,  et  al. Tensorflow: Large-scale machine learning  on  heterogeneous  distributed  systems. arXiv
             Preprint arXiv: 1603.04467, 2016.
        [113]     Cui H, Zhang H, Ganger GR, et al. GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server.
             In: Proc. of the EuroSys. 2016. 1−16.

         附中文参考文献:
         [16]  刘念,孙娜,张楠.基于 Particle Level Set 的流体模拟与并行实现.计算机工程与应用,2007,43(6):69−71.
         [17]  周世哲,满家巨.基于多重网格法的实时流体模拟.计算机辅助设计与图形学学报,2007,19(7):935−940.
         [20]  陈曦,王章野,何戬,等.GPU 中的流体场景实时模拟算法.计算机辅助设计与图形学学报,2010,22(3):396−405.
         [21]  柳有权,刘学慧,吴恩华.基于 GPU 带有复杂边界的三维实时流体模拟.软件学报,2006,17(3):568−576. http://www.jos.org.cn/
             1000-9825/17/568.htm
         [22]  李建明,吴云龙,迟忠先,等.基于流体模型和 GPU 加速的火焰实时仿真.系统仿真学报,2007,19(19):4382−4385.
         [23]  邱宇峰,曾国荪.一种基于 GPU 的粒子系统火焰模拟.计算机科学,2009,36(12):238−242.
         [28]  金季强,刘丽.基于 GPU 的二维流体实时模拟.系统仿真学报,2011,23(8):1657−1659.
         [29]  谭捷,杨旭波.基于物理的流体动画综述.中国科学(F 辑:信息科学),2009,52:723–740.
         [30]  柳有权,王章野,朱鉴,等.基于物理的流体动画加速技术的研究进展.计算机辅助设计与图形学学报,2013,25(3):312−321.
         [60]  杨贲.形状可控的烟雾动画[博士学位论文].杭州:浙江大学,2014.
         [74]  商柳,冯笑冰,朱登明,等.一种骨架驱动的近岸涌浪动画合成方法.软件学报,2016,27(10):2600−2611. http://www.jos.org.cn/1000-
             9825/5081.htm [doi: 10.13328/j.cnki.jos.005081]
        [108]  肖祥云.基于深度神经网络的流体动画研究[博士学位论文].上海:上海交通大学,2019.


                       肖祥云(1989-),男,博士,CCF 学生会员,                    杨旭波(1971-),男,博士,教授,博士生导
                       主要研究领域为计算机图形学,机器学习.                          师,CCF 高级会员,主要研究领域为计算机
                                                                    图形学,虚拟现实.
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