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  1. Home > Articles & Issues >
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  3. Input Regularization ...
Article

Input Regularization for Integer Optimal Control in BV with Applications to Control of Poroelastic and Poroviscoelastic Systems

Lorena Bociu, Paul Manns, Marvin Severitt, Sarah Strikwerda
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Open on arXiv
Submitted on
December 21, 2022
Accepted on
April 9, 2024
Published on
April 29, 2024
Last modified on
April 29, 2024
Volume 5
Volume 5
Original research articles
DOI
10.46298/jnsao-2024-10529
License
Attribution 4.0 International (CC BY 4.0)
National Science Foundation
1555062
CAREER: Control and Sensitivity Analysis for Fluid-Elasticity Interactions and Fluid-Solid Mixtures
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572
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Input Regularization for Integer Optimal Control in BV with Applications to Control of Poroelastic and Poroviscoelastic Systems

Lorena Bociu, Paul Manns, Marvin Severitt, Sarah Strikwerda
Abstract
We revisit a class of integer optimal control problems for which a trust-region method has been proposed and analyzed in arXiv:2106.13453v3 [math.OC]. While the algorithm proposed in arXiv:2106.13453v3 [math.OC] successfully solves the class of optimization problems under consideration, its convergence analysis requires restrictive regularity assumptions. There are many examples of integer optimal control problems involving partial differential equations where these regularity assumptions are not satisfied. In this article we provide a way to bypass the restrictive regularity assumptions by introducing an additional partial regularization of the control inputs by means of mollification and proving a $\Gamma$-convergence-type result when the support parameter of the mollification is driven to zero. We highlight the applicability of this theory in the case of fluid flows through deformable porous media equations that arise in biomechanics. We show that the regularity assumptions are violated in the case of poro-visco-elastic systems, and thus one needs to use the regularization of the control input introduced in this article. Associated numerical results show that while the homotopy can help to find better objective values and points of lower instationarity, the practical performance of the algorithm without the input regularization may be on par with the homotopy.
Keywords
  • Mathematics - Optimization and Control
Mathematics Subject Classification 2020
  • 49J20 - Existence theories for optimal control problems involving partial differential equations
  • 49J45 - Methods involving semicontinuity and convergence; relaxation
  • 76M30 - Variational methods applied to problems in fluid mechanics
  • 76S05 - Flows in porous media; filtration; seepage
Cited by

Source: OpenCitations

  • Homotopy trust-region method for phase-field approximations in perimeter-regularized binary optimal control

    ESAIM Control Optimisation and Calculus of Variations

    Authors : Paul Manns ORCID, Vanja Nikolić ORCID

    Journal reference : Volume 31, 2024, pp. 1

    DOI : 10.1051/cocv/2024075
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