Optimal control for a nonlinear stochastic PDE model of cancer growth

We study an optimal control problem for a stochastic model of tumour growth with drug application. This model consists of three stochastic hyperbolic equations describing the evolution of tumour cells. It also includes two stochastic parabolic equations describing the diffusions of nutrient and drug concentrations. Since all systems are subject to many uncertainties, we have added stochastic terms to the deterministic model to consider the random perturbations. Then, we have added control variables to the model according to the medical concepts to control the concentrations of drug and nutrient. In the optimal control problem, we have defined the stochastic and deterministic cost functions and we have proved the problems have unique optimal controls. For deriving the necessary conditions for optimal control variables, the stochastic adjoint equations are derived. We have proved the stochastic model of tumour growth and the stochastic adjoint equations have unique solutions. For proving the theoretical results, we have used a change of variable which changes the stochastic model and adjoint equations (a.s.) to deterministic equations. Then we have employed the techniques used for deterministic ones to prove the existence and uniqueness of optimal control.

Automated summary

This study investigates the optimal control problem for a stochastic model of tumour growth with drug application. The model consists of three stochastic hyperbolic equations for the tumour cells' evolution, and two stochastic parabolic equations for the diffusions of nutrient and drug concentrations. Stochastic terms are added to account for uncertainties, and control variables are added to control the drug and nutrient concentrations. The study proves the existence of unique optimal controls, derives necessary conditions using stochastic adjoint equations, and transforms the stochastic model and adjoint equations into deterministic ones to prove the existence and uniqueness of the optimal control.