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Besides the assimilation problem, there is the problem of our refusal to admit new immigrants other than the very small number allowed at present.
The assimilation problem then involves first solving two systems, called backward and forward systems, to determine the representers.
Discretization of the assimilation problem, which is entirely described in the general continuous case, is performed as a last step, consistent with that of the hydrodynamic problem.
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In a variational approach the data assimilation problem is posed as a minimization problem, which requires the sensitivity (derivatives) of a cost functional with respect to problem parameters.
This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model.
The augmented Lagrangian approach leads to a different formulation of the variational data assimilation problem than the weakly constrained 4D-Var.
The approach formulates a continuous meta-optimization problem for parameters; the meta-optimization is constrained by the original data assimilation problem.
This article deals with the following data assimilation problem: construct an analytical approximation of a numerical constitutive law in three-dimensional nonlinear elasticity.
The nonlinear estimation method therefore should be thought of as a natural way to access the nonlinear data assimilation problem.
Starting from the hydrodynamic model, which is represented by a linearized wave equation, we emphasize the different steps taken to lead to the final finite-element discrete system of the coupled hydrodynamic and assimilation problem.
The quasi-Newton BFGS algorithm is adapted to construct the covariance matrix of the optimal solution error using the inverse Hessian of an auxiliary data assimilation problem based on the tangent linear model constraints.
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