Model Reduction For Parametrized Optimal Control Problems in Environmental Marine Sciences and Engineering
For researchers in environmental marine sciences and engineering, it provides a computationally efficient tool for solving parametrized optimal control problems, though the approach is incremental (POD-Galerkin applied to a known problem class).
The paper proposes reduced order methods (POD-Galerkin) for parametrized optimal control problems governed by PDEs, demonstrating computational savings on two environmental applications: pollutant control in the Gulf of Trieste and solution tracking for North Atlantic Ocean dynamics.
We propose reduced order methods as a suitable approach to face parametrized optimal control problems governed by partial differential equations, with applications in en- vironmental marine sciences and engineering. Environmental parametrized optimal control problems are usually studied for different configurations described by several physical and/or geometrical parameters representing different phenomena and structures. The solution of parametrized problems requires a demanding computational effort. In order to save com- putational time, we rely on reduced basis techniques as a reliable and rapid tool to solve parametrized problems. We introduce general parametrized linear quadratic optimal control problems, and the saddle-point structure of their optimality system. Then, we propose a POD-Galerkin reduction of the optimality system. Finally, we test the resulting method on two environmental applications: a pollutant control in the Gulf of Trieste, Italy and a solution tracking governed by quasi-geostrophic equations, in its linear and nonlinear version, describing North Atlantic Ocean dynamic. The two experiments underline how reduced order methods are a reliable and convenient tool to manage several environmental optimal control problems, for different mathematical models, geographical scale as well as physical meaning.