A note on data-driven contaminant simulation

Craig C. Douglas*, Chad E. Shannon, Yalchin Efendiev, Richard Ewing, Victor Ginting, Raytcho Lazarov, Martin J. Cole, Greg Jones, Chris R. Johnson, Jennifer Simpson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


In this paper we introduce a numerical procedure for performing dynamic data driven simulations (DDDAS). The main ingredient of our simulation is the multiscale interpolation technique that maps the sensor data into the solution space. We test our method on various synthetic examples. In particular we show that frequent updating of the sensor data in the simulations can significantly improve the prediction results and thus important for applications. The frequency of sensor data updating in the simulations is related to streaming capabilities and addressed within DDDAS framework. A further extension of our approach using local inversion is also discussed. Springer-Verlag 2004.

Original languageEnglish (US)
Pages (from-to)701-708
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
StatePublished - Dec 1 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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