1. Technical Note: PDE-constrained Optimization Formulation for Tumor Growth Model Calibration
- Author
-
Liang, Baoshan, Lozenski, Luke, Villa, Umberto, and Faghihi, Danial
- Subjects
Computational Engineering, Finance, and Science (cs.CE) ,FOS: Computer and information sciences ,Optimization and Control (math.OC) ,FOS: Mathematics ,Computer Science - Computational Engineering, Finance, and Science ,Mathematics - Optimization and Control - Abstract
We discuss solution algorithms for calibrating a tumor growth model using imaging data posed as a deterministic inverse problem. The forward model consists of a nonlinear and time-dependent reaction-diffusion partial differential equation (PDE) with unknown parameters (diffusivity and proliferation rate) being spatial fields. We use a dimension-independent globalized, inexact Newton Conjugate Gradient algorithm to solve the PDE-constrained optimization. The required gradient and Hessian actions are also presented using the adjoint method and Lagrangian formalism.
- Published
- 2023