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Model order reduction for optimality systems through empirical gramians

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2023

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Deutsche Forschungsgemeinschaft (DFG): VO 1658/6-1

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Frontiers in Applied Mathematics and Statistics. Frontiers. 2023, 9, 1144142. eISSN 2297-4687. Available under: doi: 10.3389/fams.2023.1144142

Zusammenfassung

In the present article, optimal control problems for linear parabolic partial differential equations (PDEs) with time-dependent coefficient functions are considered. One of the common approach in literature is to derive the first-order sufficient optimality system and to apply a finite element (FE) discretization. This leads to a specific linear but high-dimensional time variant (LTV) dynamical system. To reduce the size of the LTV system, we apply a tailored reduced order modeling technique based on empirical gramians and derived directly from the first-order optimality system. For testing purpose, we focus on two specific examples: a multiobjective optimization and a closed-loop optimal control problem. Our proposed methodology results to be better performing than a standard proper orthogonal decomposition (POD) approach for the above mentioned examples.

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510 Mathematik

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model order reduction, empirical gramians, proper orthogonal decomposition, parabolic partial differential equations, multiobjective optimization, model predictive control, a-posteriori error analysis

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ISO 690MECHELLI, Luca, Jan ROHLEFF, Stefan VOLKWEIN, 2023. Model order reduction for optimality systems through empirical gramians. In: Frontiers in Applied Mathematics and Statistics. Frontiers. 2023, 9, 1144142. eISSN 2297-4687. Available under: doi: 10.3389/fams.2023.1144142
BibTex
@article{Mechelli2023-12-11Model-69056,
  year={2023},
  doi={10.3389/fams.2023.1144142},
  title={Model order reduction for optimality systems through empirical gramians},
  volume={9},
  journal={Frontiers in Applied Mathematics and Statistics},
  author={Mechelli, Luca and Rohleff, Jan and Volkwein, Stefan},
  note={Article Number: 1144142}
}
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