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BEGIN:VEVENT
DTSTAMP:20250822T115807Z
LOCATION:Room 5.0B56
DTSTART;TZID=Europe/Stockholm:20250616T170000
DTEND;TZID=Europe/Stockholm:20250616T180000
UID:submissions.pasc-conference.org_PASC25_sess168@linklings.com
SUMMARY:AP1C - ACM Papers Session 1C
DESCRIPTION:Scalable Bayesian Inference of Large Simulations via Asynchron
 ous Prefetching Multilevel Delayed Acceptance\n\nBayesian inference enable
 s greater scientific insight into simulation models, determining model par
 ameters and meaningful confidence regions from observed data. With hierarc
 hical methods like Multilevel Delayed Acceptance (MLDA) drastically reduci
 ng compute cost, sampling Bayesian posteriors for com...\n\n\nMaximilian K
 ruse (Karlsruhe Institute of Technology); Zihua Niu (Ludwig Maximilian Uni
 versity of Munich); Sebastian Wolf (Technical University of Munich); Mikke
 l Lykkegaard (Danish Technological Institute); Michael Bader (Technical Un
 iversity of Munich); Alice-Agnes Gabriel (University of California San Die
 go, Ludwig Maximilian University of Munich); and Linus Seelinger (Karlsruh
 e Institute of Technology)\n---------------------\nData Assimilation for R
 obust UQ Within Agent-Based Simulation on HPC Systems\n\nAgent-based simul
 ation provide a powerful tool for in silico system modeling. However, thes
 e simulations do not provide built-in methods for uncertainty quantificati
 on (UQ). A typical approach to UQ within these types of models is to run m
 ultiple realizations of the model, then compute aggregate sta...\n\n\nAdam
  Spannaus, Sifat Moon, John Gounley, and Heidi Hanson (Oak Ridge National 
 Laboratory)\n\nDomain: Computational Methods and Applied Mathematics\n\nSe
 ssion Chair: Tobias Ribizel (Technical University of Munich)
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