Deep quench approximation and optimal control of general Cahn-Hilliard systems with fractional operators and double obstacle potentials
Pierluigi Colli, Gianni Gilardi, J\"urgen Sprekels

TL;DR
This paper extends the analysis of Cahn-Hilliard systems with fractional operators and double obstacle potentials, focusing on optimal control problems and introducing the deep quench approximation to handle nondifferentiable nonlinearities.
Contribution
It develops a deep quench approximation method for optimal control of Cahn-Hilliard systems with non-differentiable double obstacle potentials, providing convergence analysis and optimality conditions.
Findings
Established convergence of the deep quench approximation.
Derived first-order optimality conditions using variational inequalities.
Demonstrated the method's effectiveness for nondifferentiable potentials.
Abstract
The paper arXiv:1804.11290 contains well-posedness and regularity results for a system of evolutionary operator equations having the structure of a Cahn-Hilliard system. The operators appearing in the system equations were fractional versions in the spectral sense of general linear operators A and B having compact resolvents and are densely defined, unbounded, selfadjoint, and monotone in a Hilbert space of functions defined in a smooth domain. The associated double-well potentials driving the phase separation process modeled by the Cahn-Hilliard system could be of a very general type that includes standard physically meaningful cases such as polynomial, logarithmic, and double obstacle nonlinearities. In the subsequent paper arXiv:1807.03218, an analysis of distributed optimal control problems was performed for such evolutionary systems, where only the differentiable case of certain…
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Deep quench approximation and optimal control
of general Cahn–Hilliard systems with fractional
operators and double obstacle potentials
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centerPierluigi Colli*(1)*
e-mail: [email protected]
Gianni Gilardi*(1)*
e-mail: [email protected]
Jürgen Sprekels*(2)*
e-mail: [email protected]
(1) Dipartimento di Matematica “F. Casorati”, Università di Pavia
and Research Associate at the IMATI – C.N.R. Pavia
via Ferrata 5, 27100 Pavia, Italy
(2) Department of Mathematics
Humboldt-Universität zu Berlin
Unter den Linden 6, 10099 Berlin, Germany
and
Weierstrass Institute for Applied Analysis and Stochastics
Mohrenstrasse 39, 10117 Berlin, Germany
