Name
Abstract | ||
|---|---|---|
Time based observations are the linchpin of improving predictions in any dynamic data driven application systems. Our predictions
are based on solutions to differential equation models with unknown initial conditions and source terms. In this paper we
want to simulate a waste spill by a water body, such as near an aquifer or in a river or bay. We employ sensors that can determine
the contaminant spill location, where it is at a given time, and where it will go. We estimate initial conditions and source
terms using better and new techniques, which improves predictions for a variety of data-driven models.
|
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/978-3-540-69389-5_8 | International Conference on Computational Science |
Keywords | Field | DocType |
differential equation model,unknown initial condition,dddas predictions,dynamic data,data-driven model,contaminant spill location,source term,application system,initial condition,water spills,waste spill,new technique,differential equation | Differential equation,Mathematical optimization,Computer science,Simulation,Dynamic data,Bay,Aquifer,Water body,Marine engineering | Conference |
Volume | ISSN | Citations |
5103 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Craig C. Douglas | 1 | 265 | 39.80 |
| Paul Dostert | 2 | 4 | 1.59 |
| Yalchin Efendiev | 3 | 581 | 67.04 |
| Richard E. Ewing | 4 | 252 | 45.87 |
| Deng Li | 5 | 0 | 0.34 |
| Robert A. Lodder | 6 | 23 | 2.72 |