Improving predictions for water spills using DDDAS

Craig C. Douglas, Paul Dostert, Yalchin Efendiev, Richard E. Ewing, Deng Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In dynamic data driven application systems, the predictions are improved based on measurements obtained in time. Predicted quantity often satisfies differential equation models with unknown initial conditions and source terms. A physical example of the problem we are attempting to solve is a major waste spill near a body of water. This can be, for example, near an aquifer, or possibly in a river or bay. Sensors can be used to measure where the contaminant was spilled, where it is, and where it will go. In this paper, we propose techniques for improving predictions by estimating initial conditions and source terms. We show how well we can solve the problem for a variety of data-driven models.

Original languageEnglish (US)
Title of host publicationIPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM
DOIs
StatePublished - 2008
Externally publishedYes
EventIPDPS 2008 - 22nd IEEE International Parallel and Distributed Processing Symposium - Miami, FL, United States
Duration: Apr 14 2008Apr 18 2008

Other

OtherIPDPS 2008 - 22nd IEEE International Parallel and Distributed Processing Symposium
CountryUnited States
CityMiami, FL
Period04/14/0804/18/08

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Electrical and Electronic Engineering

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