GAMES Webinar 2024 – 320期(高效真实感流体仿真专题) | 苏浩哲(腾讯光子工作室)，Yitong Deng(Stanford University)
【GAMES Webinar 2024-320期】(模拟与动画专题-高效真实感流体仿真专题)
报告嘉宾：苏浩哲(腾讯光子工作室)
报告时间：2024年4月25号星期四晚上8:00-9:00（北京时间）
报告题目：Real-time Height-field Simulation of Sand and Water Mixtures
报告摘要：
We propose a height-field-based real-time simulation method for sand and water mixtures. Inspired by the shallow-water assumption, our approach extends the governing equations to handle two-phase flows of sand and water using height fields. Our depth-integrated governing equations can model the elastoplastic behavior of sand, as well as sand-water-mixing phenomena such as friction, diffusion, saturation, and momentum exchange. We further propose an operator-splitting time integrator that is both GPU-friendly and stable under moderate time step sizes. We have evaluated our method on a set of benchmark scenarios involving large bodies of heterogeneous materials, where our GPU-based algorithm runs at real-time frame rates. Our method achieves a desirable trade-off between fidelity and performance, bringing an unprecedentedly immersive experience for real-time applications.
讲者简介：
Haozhe Su is a senior researcher at LightSpeed Studios, Tencent America. He received his Ph.D. degree in Computer Science from Rutgers University, under the supervision of Prof. Mridul Aanjaneya. His research interests lie in numerical computation/algorithms, and their application in physics-based simulation.
个人主页：https://soldierdown.github.io/
报告嘉宾：Yitong Deng(Stanford University)
报告时间：2024年4月25号星期四晚上9:00-10:00（北京时间）
报告题目：Fluid Simulation on Neural Flow Maps
报告摘要：
We introduce Neural Flow Maps, a novel simulation method bridging the emerging paradigm of implicit neural representations with fluid simulation based on the theory of flow maps, to achieve state-of-the-art simulation of inviscid fluid phenomena. We devise a novel hybrid neural field representation, Spatially Sparse Neural Fields (SSNF), which fuses small neural networks with a pyramid of overlapping, multi-resolution, and spatially sparse grids, to compactly represent long-term spatiotemporal velocity fields at high accuracy. With this neural velocity buffer in hand, we compute long-term, bidirectional flow maps and their Jacobians in a mechanistically symmetric manner, to facilitate drastic accuracy improvement over existing solutions. These long-range, bidirectional flow maps enable high advection accuracy with low dissipation, which in turn facilitates high-fidelity incompressible flow simulations that manifest intricate vortical structures. We demonstrate the efficacy of our neural fluid simulation in a variety of challenging simulation scenarios, including leapfrogging vortices, colliding vortices, vortex reconnections, as well as vortex generation from moving obstacles and density differences. Our examples show increased performance over existing methods in terms of energy conservation, visual complexity, adherence to experimental observations, and preservation of detailed vortical structures.
讲者简介：
Yitong Deng is a first-year Ph.D. student at Stanford University, specializing in physical simulation in computer graphics. He is particularly interested in bridging physics first-principles with machine learning capacities to build novel world models to not only create cutting-edge visual effects for arts and entertainment, but also facilitate solutions to new, physically-based inverse design and control problems.
个人主页：https://yitongdeng.github.io
主持人简介：
任博，南开大学计算机学院副教授。主要研究方向包括计算机图形学基于物理/机器学习的仿真与控制，神经辐射场三维场景重建与渲染等。在国际顶级期刊会议（中科院一区，CCFA类）发表文章二十余篇。入选中国图学学会青年托举计划，天津市“131”人才梯队，南开大学百名青年学科带头人培养计划。主持或参与多项国家自然科学基金青年/面上项目，国家重点研发计划课题与国家重点实验室开放课题。担任多个图形学国际会议如SIGGRAPHAsia，CVM，PacificGraphics等的分会场主席。
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观看直播的链接： https://live.bilibili.com/h5/24617282
