Multiobjective Optimal Control of Partial Differential Equations Using Reduced-Order Modeling

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In almost all technical applications, multiple criteria are of interest – both during development as well as operation. Examples are fast butenergy efficient vehicles and constructions that have to be light as well as stable. The goal in the resulting multiobjective optimizationproblems is the computation of the set of optimal compromises – the so-called Pareto set. A decision maker can then select an appropriatesolution from this set. In control applications, it is possible to quickly switch between different compromises as a reaction to changes in theexternal conditions. The Pareto set generally consists of infinitely many compromise solutions, its numerical approximation is thereforeconsiderably more expensive than the solution of scalar optimization problems. This can quickly result in prohibitively large computationalcost, particularly in situations where solutions to the underlying systems are computationally expensive. For instance, this is the casewhen the system is described by a partial differential equation (PDE).In this context, surrogate models are frequently used that can be solved significantly faster than classical numerical approximations bythe finite element method. In the case of non-smooth PDEs, reducing the computational cost is particularly important since these problemsare often significantly more expensive to solve than smooth problems. However, the surrogate models introduce an approximation error intothe system, which has to be quantified and considered both in the analysis and the development of numerical algorithms. For nonsmoothproblems, literature on this topic is currently scarce. The goal of this project is the development of efficient numerical methods to solve multiobjective optimization problems that are constrained by certain classes of non-smooth PDEs. In the first step, optimality conditions for the non-smooth PDE-constrained problems will be derived, and the (hierarchical) structure of the Pareto sets will be analyzed. Building on this, algorithms for the computation of Pareto sets will be developed for these problems. The methods will be used for the optimization of problems with max-terms, contact problems, and time dependent hybrid and switched systems. In order to handle the numerical effort, reduced order modeling techniques – such as Reduced Basis, Proper Orthogonal Decomposition and more recent approaches based on the Koopman operator will be extended to the non-smooth setting. This requires the consideration of inexactness in the convergence analysis. Finally, the algorithms will be applied to several different problem settings in cooperation with other members of the Priority Programme.