About: Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation
Creator	Cho, Yubin Huang, Yilin Verbraeck, Alexander
source	PMC
abstract	Dynamic data-driven simulation (DDDS) incorporates real-time measurement data to improve simulation models during model run-time. Data assimilation (DA) methods aim to best approximate model states with imperfect measurements, where particle Filters (PFs) are commonly used with discrete-event simulations. In this paper, we study three critical conditions of DA using PFs: (1) the time interval of iterations, (2) the number of particles and (3) the level of actual and perceived measurement errors (or noises), and provide recommendations on how to strategically use data assimilation for DDDS considering these conditions. The results show that the estimation accuracy in DA is more constrained by the choice of time intervals than the number of particles. Good accuracy can be achieved without many particles if the time interval is sufficiently short. An over estimation of the level of measurement errors has advantages over an under estimation. Moreover, a slight over estimation has better estimation accuracy and is more responsive to system changes than an accurate perceived level of measurement errors.
has issue date	2020-05-25(xsd:dateTime)
bibo:doi	10.1007/978-3-030-50433-5_3
has license	no-cc
sha1sum (hex)	f2a549fda9d3b79dfcfe60f0028fb4862f5d1504
schema:url	https://doi.org/10.1007/978-3-030-50433-5_3
resource representing a document's title	Strategic Use of Data Assimilation for Dynamic Data-Driven Simulation
has PubMed Central identifier	PMC7304738
schema:publication	Computational Science - ICCS 2020
resource representing a document's body	covid:f2a549fda9d3b79dfcfe60f0028fb4862f5d1504#body_text
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