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BEGIN:VEVENT
DTSTAMP:20260522T150111Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post133@linklings.com
SUMMARY:Machine Learning for Adaptive Discretization in Massive Multiscale
  Biomedical Modeling
DESCRIPTION:Changnian Han, Prachi Gupta, Peng Zhang, Danny Bluestein, and 
 Yuefan Deng (Stony Brook University)\n\nFor multiscale problems, tradition
 al time stepping algorithms use a single smallest time stepsize in order t
 o capture the finest details; using this scale leads to a significant wast
 e of computing resources for simulating coarse-grained portion of the prob
 lem. To improve computing efficiency for multiscale modeling, we propose a
  novel state-driven adaptive time stepping (ATS) algorithm to automaticall
 y adapt the time stepsizes to the underlying biophysical phenomena at mult
 iple scales. In this, we use a machine-learning based solution framework t
 o classify and label these states for regulating the time stepsizes. We de
 monstrate the values of our ATS algorithm by assessing the accuracy and ef
 ficiency of a multiscale two-platelet aggregation simulation. By comparing
  with traditional algorithm for this simulation, our ATS algorithm signifi
 cantly improves the efficiency while maintaining accuracy. Our novel ATS a
 lgorithm presents a more efficient framework for solving massive multiscal
 e biomedical problems.\n\nRegistration Category: Tech Program Reg Pass, Ex
 hibits Reg Pass\n\n
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