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DTSTAMP:20240214T070246Z
LOCATION:Meeting Room C4.9+C4.10\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231214T095000
DTEND;TZID=Australia/Melbourne:20231214T100500
UID:siggraphasia_SIGGRAPH Asia 2023_sess165_tog_101@linklings.com
SUMMARY:Real-Time Reconstruction of Fluid Flow under Unknown Disturbance
DESCRIPTION:Technical Papers, TOG\n\nKinfung Chu (Tohoku University); Jiaw
 ei Huang (Chuzhou University, Void Dimensions); and Hidemasa Takana and Yo
 shifumi Kitamura (Tohoku University)\n\nWe present a framework that captur
 es sparse Lagrangian flow information from a volume of real liquid and rec
 onstructs its detailed kinematic information in real time. Our framework c
 an perform flow reconstruction even when the liquid is disturbed by an obj
 ect of unknown movement and shape. Through a large dataset of liquid movin
 g under external disturbance, an agent is trained using reinforcement lear
 ning to reproduce the target flow kinematics with only the captured sparse
  information as inputs while remaining oblivious to the movement and the s
 hape of the disturbance sources. To ensure that the underlying simulation 
 model faithfully obeys physical reality, we also optimize the viscosity pa
 rameters in Smoothed Particle Hydrodynamics (SPH) using classical fluid dy
 namics knowledge and gradient-based optimization. By quantitatively compar
 ing the reconstruction results against real-world and simulated ground tru
 th, we verified that our reconstruction method is resilient to different a
 gitation patterns.\n\nRegistration Category: Full Access
URL:https://asia.siggraph.org/2023/full-program?id=tog_101&sess=sess165
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