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DTSTAMP:20250822T115807Z
LOCATION:Room 5.0B56
DTSTART;TZID=Europe/Stockholm:20250616T173000
DTEND;TZID=Europe/Stockholm:20250616T180000
UID:submissions.pasc-conference.org_PASC25_sess168_pap122@linklings.com
SUMMARY:Data Assimilation for Robust UQ Within Agent-Based Simulation on H
 PC Systems
DESCRIPTION:Adam Spannaus, Sifat Moon, John Gounley, and Heidi Hanson (Oak
  Ridge National Laboratory)\n\nAgent-based simulation provide a powerful t
 ool for in silico system modeling. However, these simulations do not provi
 de built-in methods for uncertainty quantification (UQ). A typical approac
 h to UQ within these types of models is to run multiple realizations of th
 e model, then compute aggregate statistics upon completion. This approach 
 is limited due to the compute time required for a solution. When faced wit
 h an emerging biothreat, public health decisions need to be made quickly a
 nd solutions for integrating near real-time data with analytic tools are n
 eeded. We propose an integrated Bayesian UQ framework for agent-based mode
 ls based on sequential Monte Carlo sampling. Given streaming or static dat
 a about the evolution of an emerging pathogen this Bayesian framework prov
 ides a distribution over the parameters governing the spread of a disease 
 through a population, yielding accurate estimates of the spread of a disea
 se to public health agencies seeking to abate the spread. By coupling agen
 t-based simulations with Bayesian modeling in a data assimilation, our pro
 posed framework provides a powerful tool for modeling dynamical systems in
  silico. We propose a method which reduces model error and provides a rang
 e of realistic possible outcomes. Our method addresses two primary limitat
 ions of ABMs: lack of UQ and inability to assimilate data. Our proposed fr
 amework combines the flexibility of an agent-based model with the rigorous
  UQ provided by the Bayesian paradigm in a workflow which scales well to H
 PC systems. We provide algorithmic details and results on a simulated outb
 reak with both static and streaming data.\n\nDomain: Computational Methods
  and Applied Mathematics\n\nSession Chair: Tobias Ribizel (Technical Unive
 rsity of Munich)\n\n
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