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DTSTAMP:20240214T070248Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T144500
DTEND;TZID=Australia/Melbourne:20231214T150000
UID:siggraphasia_SIGGRAPH Asia 2023_sess151_papers_776@linklings.com
SUMMARY:Fluid Simulation on Neural Flow Maps
DESCRIPTION:Technical Papers\n\nYitong Deng (Dartmouth College), Hong-Xing
  Yu (Stanford University), Diyang Zhang (Dartmouth College), Jiajun Wu (St
 anford University), and Bo Zhu (Dartmouth College)\n\nWe introduce Neural 
 Flow Maps, a novel simulation method bridging the emerging paradigm of imp
 licit neural representations with fluid simulation based on the theory of 
 flow maps, to achieve state-of-the-art simulation of inviscid fluid phenom
 ena. We devise a novel hybrid neural field representation, Spatially-spars
 e Neural Fields (SNF), which fuses small neural networks with a pyramid of
  overlapping, multi-resolution, and spatially-sparse grids, that compactly
  represents long-term spatiotemporal velocity fields at high precision. Wi
 th this neural velocity buffer at hand, we compute long-term, bidirectiona
 l flow maps and their Jacobians in a mechanistically symmetric manner, to 
 facilitate drastic accuracy improvement over existing solutions. These lon
 g-range, bidirectional flow maps enable high advection accuracy with low d
 issipation, which in turn facilitates high-fidelity incompressible flow si
 mulations that manifest intricate vortical structures. We demonstrate the 
 efficacy of our neural fluid simulation in a variety of challenging simula
 tion scenarios, including leapfrogging vortices, colliding vortices, vorte
 x reconnections, as well as vortex generation from moving obstacles and de
 nsity differences. Our examples show increased performance over existing m
 ethods in terms of energy conservation, visual complexity, adherence to ex
 perimental observations, and preservation of detailed vortical structures.
 \n\nRegistration Category: Full Access\n\nSession Chair: Tao Du (Tsinghua 
 University, Shanghai Qi Zhi Institute)
URL:https://asia.siggraph.org/2023/full-program?id=papers_776&sess=sess151
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