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We discuss solution algorithms for calibrating a tumor growth model usingimaging data posed as a deterministic inverse problem. The forward modelconsists of a nonlinear and time-dependent reaction-diffusion partialdifferential equation (PDE) with unknown parameters (diffusivity andproliferation rate) being spatial fields. We use a dimension-independentglobalized, inexact Newton Conjugate Gradient algorithm to solve thePDE-constrained optimization. The required gradient and Hessian actions arealso presented using the adjoint method and Lagrangian formalism.

Technical Note: PDE-constrained Optimization Formulation for Tumor Growth Model Calibration