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DTSTAMP:20240214T070241Z
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:Technical Papers\n\nKinfung Chu (Tohoku University); Jiawei Hu
 ang (Chuzhou University, Void Dimensions); and Hidemasa Takana and Yoshifu
 mi Kitamura (Tohoku University)\n\nWe present a framework that captures sp
 arse Lagrangian flow information from a volume of real liquid and reconstr
 ucts its detailed kinematic information in real time. Our framework can pe
 rform flow reconstruction even when the liquid is disturbed by an object o
 f unknown movement and shape. Through a large dataset of liquid moving und
 er external disturbance, an agent is trained using reinforcement learning 
 to reproduce the target flow kinematics with only the captured sparse info
 rmation as inputs while remaining oblivious to the movement and the shape 
 of the disturbance sources. To ensure that the underlying simulation model
  faithfully obeys physical reality, we also optimize the viscosity paramet
 ers in Smoothed Particle Hydrodynamics (SPH) using classical fluid dynamic
 s knowledge and gradient-based optimization. By quantitatively comparing t
 he reconstruction results against real-world and simulated ground truth, w
 e verified that our reconstruction method is resilient to different agitat
 ion patterns.\n\nRegistration Category: Full Access, Enhanced Access, Trad
 e Exhibitor, Experience Hall Exhibitor
URL:https://asia.siggraph.org/2023/full-program?id=tog_101&sess=sess209
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