

Craig Douglas : Dyanamic DataDriven Application Simulations (DDDAS)
DDDAS is a new paradigm in which data dynamically
controls almost all aspects of long term simulations.
Rather than run many simulations using static data as
initial conditions, a very small number of simulations
are run with additional data injected as it becomes
available. The dynamic data is used to determine
 whether or not a warm restart is necessary due
to unacceptable errors building up in parts of
the domain,
 if a rollback in time is required, or
 if the simulation is running with acceptable
errors.
Ideally, there does not have to be a human in the
control loop throughout a simulation.
Using the data appropriately lets the physical and
mathematical models, the discretization, and the scales
of interesting parts of the computations become
parameters that can be changed during the course of the
simulation. In addition, error propogation is of
particular interest in nonlinear time dependent
simulations.
DDDAS offers interesting computational and
mathematically unsolved problems, such as, how do you
analyze the properties 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?
