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DTSTART;TZID=America/Chicago:20251118T080000
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UID:submissions.supercomputing.org_SC25_sess527_post174@linklings.com
SUMMARY:Divergence Prediction System for CFD Simulations
DESCRIPTION:Takashi Soga (The University of Osaka), Takanori Uchida (Kyush
 u University), and Susumu Date (The University of Osaka)\n\nComputational 
 fluid dynamics (CFD) simulations are essential tools for analyzing complex
  flow phenomena in engineering and scientific research. These simulations 
 are typically formulated based on the Navier-Stokes equations, which gover
 n the motion of incompressible fluids, and the pressure field is obtained 
 by solving the Poisson equation using iterative solvers. However, iterativ
 e convergence is not always guaranteed. In certain cases, the residuals di
 verge, leading to numerical instability and eventual simulation failure. W
 hen divergence occurs after tens of thousands of time steps, it results in
  substantial waste of computational resources and delays research progress
 . To address this problem, this study proposes an AI-based divergence pred
 iction system. By utilizing learned data from prior simulations, the propo
 sed method enables prediction of divergence within about one hundred time 
 steps. This early detection allows simulations to be interrupted before si
 gnificant resources are consumed, thereby improving efficiency and support
 ing timely progress in computational research.\n\nTag: Research & ACM SRC 
 Posters\n\nRegistration Category: Technical Program Reg Pass\n\nSession Ch
 airs: Kento Sato (RIKEN Center for Computational Science (R-CCS)); Chris S
 chlipalius (Pawsey Supercomputing Research Centre; Commonwealth Scientific
  and Industrial Research Organisation (CSIRO), Australia); and Anja Gerbes
  (Georg-August-Universität Göttingen)\n\n
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