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| 1 | Visual Simulation of Multiple Fluids in Computer Graphics: A State-of-the-Art Report显示文摘在多重液体之间的各种各样的相互作用的现实主义的动画,可能经历阶段变化,是在计算机图形的一项挑战性的任务。多相的多液体现象的视觉范围盖住复杂纠缠的表面结构和富有的颜色变化,它能极大地在图形应用提高视觉效果。描述如此的现象要求更复杂的模型处理包含相互作用,动力学和多重阶段的空间分发的计算的挑战,它经常被包含并且难获得即时性能。最近,算法的一个多样的集合被介绍了基于管理实现复杂多液体现象物理法律和新奇 discretization 方法到当保证时,加速全面计算数字稳定性。由在多重液体的宽广题目通过最近的研究的目标现象排序,这份最先进的报告在计算机图形在多液体模拟上总结最近的进展。 | Bo Ren Xu-Yun Yang Ming C. Lin Nils Thuerey Matthias Teschner Chenfeng Li | 2018 | Journal of Computer Science & Technology2018，33，3： | 2 |
| 2 | Lagrangian vortex sheets for ani- mating fluids 显示文摘 | Pfaff T Thuerey N Gross M | 2012 | ACM Transactions on Graphics2012，31，4： | 1 |
| 3 | Scalable fluid simulation using anisotropic turbulence particles显示文摘 | Tobias Pfaff Nils Thuerey Jonathan Cohen Sarah Tariq Markus Gross | 2010 | ACM Transactions on Graphics (TOG)2010，，6： | 1 |
| 4 | Physics-Driven Learning of the Steady Navier-Stokes Equations using Deep Convolutional Neural Networks显示文摘Recently,physics-driven deep learning methods have shown particular promise for the prediction of physical fields,especially to reduce the dependency on large amounts of pre-computed training data.In this work,we target the physicsdriven learning of complex flow fields with high resolutions.We propose the use of Convolutional neural networks(CNN)based U-net architectures to efficiently represent and reconstruct the input and output fields,respectively.By introducingNavier-Stokes equations and boundary conditions into loss functions,the physics-driven CNN is designed to predict corresponding steady flow fields directly.In particular,this prevents many of the difficulties associated with approaches employing fully connected neural networks.Several numerical experiments are conducted to investigate the behavior of the CNN approach,and the results indicate that a first-order accuracy has been achieved.Specifically for the case of a flow around a cylinder,different flow regimes can be learned and the adhered“twin-vortices”are predicted correctly.The numerical results also show that the training for multiple cases is accelerated significantly,especially for the difficult cases at low Reynolds numbers,and when limited reference solutions are used as supplementary learning targets. | Hao Ma Yuxuan Zhang Nils Thuerey Xiangyu Hu Oskar J.Haidn | 2022 | Communications in Computational Physics2022，32，8： | 0 |
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