Technical Note: PDE-constrained Optimization Formulation for Tumor Growth Model Calibration
Computational Engineering, Finance, and Science
2023-02-17 v2 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.
Keywords
Cite
@article{arxiv.2302.06445,
title = {Technical Note: PDE-constrained Optimization Formulation for Tumor Growth Model Calibration},
author = {Baoshan Liang and Luke Lozenski and Umberto Villa and Danial Faghihi},
journal= {arXiv preprint arXiv:2302.06445},
year = {2023}
}