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DTSTAMP:20260114T163633Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_tog_101@linklings.com
SUMMARY:Real-Time Reconstruction of Fluid Flow under Unknown Disturbance
DESCRIPTION:Kinfung Chu (Tohoku University); Jiawei Huang (Chuzhou Univers
 ity, Void Dimensions); and Hidemasa Takana and Yoshifumi Kitamura (Tohoku 
 University)\n\nWe present a framework that captures sparse Lagrangian flow
  information from a volume of real liquid and reconstructs its detailed ki
 nematic information in real time. Our framework can perform flow reconstru
 ction even when the liquid is disturbed by an object of unknown movement a
 nd shape. Through a large dataset of liquid moving under external disturba
 nce, an agent is trained using reinforcement learning to reproduce the tar
 get flow kinematics with only the captured sparse information as inputs wh
 ile remaining oblivious to the movement and the shape of the disturbance s
 ources. To ensure that the underlying simulation model faithfully obeys ph
 ysical reality, we also optimize the viscosity parameters in Smoothed Part
 icle Hydrodynamics (SPH) using classical fluid dynamics knowledge and grad
 ient-based optimization. By quantitatively comparing the reconstruction re
 sults against real-world and simulated ground truth, we verified that our 
 reconstruction method is resilient to different agitation patterns.\n\nReg
 istration Category: Full Access, Enhanced Access, Trade Exhibitor, Experie
 nce Hall Exhibitor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=tog_101&sess=sess209
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