Variance-reduced simulation of stochastic agent-based models for tumor growth

TL;DR

This paper presents a hybrid PDE/Monte Carlo method that significantly reduces variance in simulating stochastic agent-based tumor growth models, improving efficiency in multiscale cancer modeling.

Contribution

It introduces a novel variance reduction technique combining stochastic agent simulations with a deterministic PDE, enhancing simulation accuracy with minimal extra computational cost.

Findings

Significant variance reduction achieved in tumor growth simulations.

Effective in both avascular and vascular tumor growth stages.

Demonstrates computational efficiency improvements.

Abstract

We investigate a hybrid PDE/Monte Carlo technique for the variance reduced simulation of an agent-based multiscale model for tumor growth. The variance reduction is achieved by combining a simulation of the stochastic agent-based model on the microscopic scale with a deterministic solution of a simplified (coarse) partial differential equation (PDE) on the macroscopic scale as a control variable. We show that this technique is able to significantly reduce the variance with only the (limited) additional computational cost associated with the deterministic solution of the coarse PDE. We illustrate the performance with numerical experiments in different regimes, both in the avascular and vascular stage of tumor growth.