Optimal control for a nonlinear stochastic PDE model of cancer growth

TL;DR

This paper develops an optimal control framework for a complex stochastic PDE model of cancer growth, incorporating uncertainties and control variables for drug and nutrient concentrations, with proven existence and uniqueness of solutions.

Contribution

It introduces a novel stochastic PDE model for tumor growth with control variables and establishes the existence and uniqueness of optimal controls and solutions.

Findings

Proved the existence and uniqueness of optimal controls.

Derived stochastic adjoint equations with unique solutions.

Converted stochastic equations to deterministic form for analysis.

Abstract

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 that 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.