Class hierarchy: Interface.

Interfaces provide access to simulation codes or, conversely, approximations based on simulation code data. In the simulation case, an ApplicationInterface is used. ApplicationInterface is specialized according to the simulation invocation mechanism, for which the following nonintrusive approaches are supported:

  • SysCallApplicInterface: the simulation is invoked using a system call (the C function system()). Asynchronous invocation utilizes a background system call. Utilizes the CommandShell utility.

  • ForkApplicInterface: the simulation is invoked using a fork (the fork/exec/wait family of functions). Asynchronous invocation utilizes a nonblocking fork.

  • SpawnApplicInterface: for Windows, fork is replaced by spawn. Asynchronous invocation utilizes a nonblocking spawn.

Fork and Spawn are inherited from ProcessHandleApplicInterface and System and ProcessHandle are inherited from ProcessApplicInterface. A semi-intrusive approach is also supported by:

  • DirectApplicInterface: the simulation is linked into the Dakota executable and is invoked using a procedure call. Asynchronous invocations will utilize nonblocking threads (capability not yet available). Specializations of the direct interface are implemented in MatlabInterface, PythonInterface, ScilabInterface, and (for built-in testers) TestDriverInterface, while examples of plugin interfaces for library mode in serial and parallel, respectively, are included in SerialDirectApplicInterface and ParallelDirectApplicInterface

Scheduling of jobs for asynchronous local, message passing, and hybrid parallelism approaches is performed in the ApplicationInterface class, with job initiation and job capture specifics implemented in the derived classes.

In the approximation case, global, multipoint, or local data fit approximations to simulation code response data can be built and used as surrogates for the actual, expensive simulation. The interface class providing this capability is

  • ApproximationInterface: builds an approximation using data from a truth model and then employs the approximation for mapping variables to responses. This class contains an array of Approximation objects, one per response function, which support a variety of approximation types using the different Approximation derived classes. These include SurfpackApproximation (provides kriging, MARS, moving least squares, neural network, polynomial regression, and radial basis functions), GaussProcApproximation (Gaussian process models), PecosApproximation (multivariate orthogonal and Lagrange interpolation polynomials from Pecos), TANA3Approximation (two-point adaptive nonlinearity approximation), and TaylorApproximation (local Taylor series).

which is an essential component within the DataFitSurrModel capability described above in Models.