Chapter

Computational Science - ICCS 2004

Volume 3038 of the series Lecture Notes in Computer Science pp 701-708

A Note on Data-Driven Contaminant Simulation

  • Craig C. DouglasAffiliated withDepartment of Computer Science, University of KentuckyDepartment of Computer Science, Yale University
  • , Chad E. ShannonAffiliated withDepartment of Computer Science, University of Kentucky
  • , Yalchin EfendievAffiliated withISC, Texas A&M University
  • , Richard EwingAffiliated withISC, Texas A&M University
  • , Victor GintingAffiliated withISC, Texas A&M University
  • , Raytcho LazarovAffiliated withISC, Texas A&M University
  • , Martin J. ColeAffiliated withScientific Computing and Imaging Institute, University of Utah
  • , Greg JonesAffiliated withScientific Computing and Imaging Institute, University of Utah
  • , Chris R. JohnsonAffiliated withScientific Computing and Imaging Institute, University of Utah
    • , Jennifer SimpsonAffiliated withScientific Computing and Imaging Institute, University of Utah

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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.