WIAS Preprint No. 2721, (2020)

Sparse optimal control of a phase field system with singular potentials arising in the modeling of tumor growth


Authors

  • Sprekels, Jürgen
    ORCID: 0009-0000-0618-8604
  • Tröltzsch, Fredi

2010 Mathematics Subject Classification

  • 49J20 49K20 49K40 35K57 37N25

Keywords

  • Sparse optimal control, tumor growth models, singular potentials, optimality conditions

DOI

10.20347/WIAS.PREPRINT.2721

Abstract

In this paper, we study an optimal control problem for a nonlinear system of reaction-diffusion equations that constitutes a simplified and relaxed version of a thermodynamically consistent phase field model for tumor growth originally introduced in [13]. The model takes the effect of chemotaxis into account but neglects velocity contributions. The unknown quantities of the governing state equations are the chemical potential, the (normalized) tumor fraction, and the nutrient extra-cellular water concentration. The equation governing the evolution of the tumor fraction is dominated by the variational derivative of a double-well potential which may be of singular (e.g., logarithmic) type. In contrast to the recent paper [10] on the same system, we consider in this paper sparsity effects, which means that the cost functional contains a nondifferentiable (but convex) contribution like the L1-norm. For such problems, we derive first-order necessary optimality conditions and conditions for directional sparsity, both with respect to space and time, where the latter case is of particular interest for practical medical applications in which the control variables are given by the administration of cytotoxic drugs or by the supply of nutrients. In addition to these results, we prove that the corresponding control-to-state operator is twice continuously differentiable between suitable Banach spaces, using the implicit function theorem. This result, which complements and sharpens a differentiability result derived in [10], constitutes a prerequisite for a future derivation of second-order sufficient optimality conditions.

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