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)



报告题目:Real-time Height-field Simulation of Sand and Water Mixtures


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.


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. 


报告嘉宾:Yitong Deng(Stanford University)


报告题目:Fluid Simulation on Neural Flow Maps


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.


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.




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