Improving predictions for water spills using DDDAS

In dynamic data driven application systems, the predictions are improved based on measurements obtained in time. Predicted quantity often satisfies differential equation models with unknown initial conditions and source terms. A physical example of the problem we are attempting to solve is a major waste spill near a body of water. This can be, for example, near an aquifer, or possibly in a river or bay. Sensors can be used to measure where the contaminant was spilled, where it is, and where it will go. In this paper, we propose techniques for improving predictions by estimating initial conditions and source terms. We show how well we can solve the problem for a variety of data-driven models.