A Note on Data-Driven Contaminant Simulation

Craig C. Douglas1,2, Chad E. Shannon1, Yalchin Efendiev3, Richard Ewing3, Victor Ginting3, Raytcho Lazarov3, Martin J. Cole4, Greg Jones4, Chris R. Johnson4, and Jennifer Simpson4

1University of Kentucky, Department of Computer Science, 325 McVey Hall, Lexington, KY 40506-0045, USA
craig.douglas@uky.edu
ceshan0@uky.edu

2Yale University, Department of Computer Science, P.O. Box 208285, New Haven, CT 06520-8285, USA
douglas-craig@cs.yale.edu

3Texas A&M University, ISC, College Station, TX, USA
efendiev@math.tamu.edu
richard-ewing@tamu.edu
ginting@math.tamu.edu
lazarov@math.tamu.edu

4Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
mjc@sci.utah.edu
gjones@sci.utah.edu
crj@cs.utah.edu
simpson@cs.utah.edu

Abstract. 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.

LNCS 3038, pp. 701-708.

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