Multiobjective Optimal Control Methods for Fluid Flow Using Reduced Order Modeling
Sebastian Peitz, Sina Ober-Bl\"obaum, Michael Dellnitz

TL;DR
This paper presents a combined approach of reduced order modeling and multiobjective optimal control to efficiently solve PDE-constrained fluid flow problems, balancing computational cost and solution quality.
Contribution
It introduces methods integrating reduced order models with multiobjective control techniques, including derivative-free and gradient-based approaches, for PDE-constrained fluid flow optimization.
Findings
Methods effectively approximate Pareto sets in fluid flow control.
Gradient-based and derivative-free methods are compared in terms of efficiency.
Application to 2D incompressible flow demonstrates practical viability.
Abstract
In a wide range of applications it is desirable to optimally control a dynamical system with respect to concurrent, potentially competing goals. This gives rise to a multiobjective optimal control problem where, instead of computing a single optimal solution, the set of optimal compromises, the so-called Pareto set, has to be approximated. When the problem under consideration is described by a partial differential equation (PDE), as is the case for fluid flow, the computational cost rapidly increases and makes its direct treatment infeasible. Reduced order modeling is a very popular method to reduce the computational cost, in particular in a multi query context such as uncertainty quantification, parameter estimation or optimization. In this article, we show how to combine reduced order modeling and multiobjective optimal control techniques in order to efficiently solve multiobjective…
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