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DTSTAMP:20260114T163631Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_776@linklings.com
SUMMARY:Fluid Simulation on Neural Flow Maps
DESCRIPTION:Yitong Deng (Dartmouth College), Hong-Xing Yu (Stanford Univer
 sity), Diyang Zhang (Dartmouth College), Jiajun Wu (Stanford University), 
 and Bo Zhu (Dartmouth College)\n\nWe introduce Neural Flow Maps, a novel s
 imulation method bridging the emerging paradigm of implicit neural represe
 ntations with fluid simulation based on the theory of flow maps, to achiev
 e state-of-the-art simulation of inviscid fluid phenomena. We devise a nov
 el hybrid neural field representation, Spatially-sparse Neural Fields (SNF
 ), which fuses small neural networks with a pyramid of overlapping, multi-
 resolution, and spatially-sparse grids, that compactly represents long-ter
 m spatiotemporal velocity fields at high precision. With this neural veloc
 ity buffer at hand, we compute long-term, bidirectional flow maps and thei
 r Jacobians in a mechanistically symmetric manner, to facilitate drastic a
 ccuracy improvement over existing solutions. These long-range, bidirection
 al flow maps enable high advection accuracy with low dissipation, which in
  turn facilitates high-fidelity incompressible flow simulations that manif
 est intricate vortical structures. We demonstrate the efficacy of our neur
 al fluid simulation in a variety of challenging simulation scenarios, incl
 uding leapfrogging vortices, colliding vortices, vortex reconnections, as 
 well as vortex generation from moving obstacles and density differences. O
 ur examples show increased performance over existing methods in terms of e
 nergy conservation, visual complexity, adherence to experimental observati
 ons, and preservation of detailed vortical structures.\n\nRegistration Cat
 egory: Full Access, Enhanced Access, Trade Exhibitor, Experience Hall Exhi
 bitor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_776&sess=sess209
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