We describe, devise, and augment dynamic data-driven application simulations (DDDAS). DDDAS offers interesting computational and mathematically unsolved problems, such as, how do you analyze, compute, and predict the solution of a generalized PDE when you do not know either where or what the boundary conditions are at any given moment in the simulation in advance? A summary of DDDAS features and why this is a intellectually stimulating new field are included in the paper. We apply the DDDAS methodology to some examples from a contaminant transport problem. We demonstrate that the multiscale interpolation and backward in time error monitoring are useful to long running simulations.
Bibliographical noteFunding Information:
This research has been supported in part by the National Science Foundations under grants EIA-0219627, EIA-0218229, and EIA-0218721.
- Automatic model changing
- Multiscale methods
- Remote supercomputing
ASJC Scopus subject areas
- Modeling and Simulation
- Computational Theory and Mathematics
- Computational Mathematics