Tensor-based numerical method for stochastic homogenisation
For researchers in computational materials science, this work provides a cost-efficient approximation for stochastic homogenization, though it is incremental as it combines existing techniques and shows clear limitations.
The paper develops a tensor-based method to reduce the computational cost of stochastic homogenization for random materials, achieving cost reduction via quasi-periodicity exploitation and multi-fidelity Monte Carlo variance reduction, with limitations shown for non-periodic materials.
This paper addresses the complexity reduction of stochastic homogenisation of a class of random materials for a stationary diffusion equation. A cost-efficient approximation of the correctors is built using a method designed to exploit quasi-periodicity. Accuracy and cost reduction are investigated for local perturbations or small transformations of periodic materials as well as for materials with no periodicity but a mesoscopic structure, for which the limitations of the method are shown. Finally, for materials outside the scope of this method, we propose to use the approximation of homogenised quantities as control variates for variance reduction of a more accurate and costly Monte Carlo estimator (using a multi-fidelity Monte Carlo method). The resulting cost reduction is illustrated in a numerical experiment with a control variate from weakly stochastic homogenisation for comparison, and the limits of this variance reduction technique are tested on materials without periodicity or mesoscopic structure.