Variance reduction for multiscale tumor growth modeling

We are interested in the mean-field evolution of a growing tumor as it emerges from a stochastic agent-based multiscale model. To this end, we introduce a hybrid PDE/Monte Carlo variance reduction technique. The variance reduction on the cell densities 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 biological scenarios.status: publishe