Microstructure-informed reduced modes synthesized with Wang tiles and the Generalized Finite Element Method

TL;DR

This paper introduces a novel reduced order modeling approach for stochastic microstructures using Wang tiles and the Generalized Finite Element Method, achieving high accuracy with minimal computational cost and flexibility for varying macroscopic conditions.

Contribution

It develops a microstructure-informed reduced mode synthesis method that does not require recalculations for different macroscopic geometries or loadings, unlike traditional snapshot-based approaches.

Findings

Less than 3% error in $L_2$ and energy norms

Uses only 0.01% of unknowns compared to full models

Handles significant macroscopic changes without offline recalculations

Abstract

A recently introduced representation by a set of Wang tiles -- a generalization of the traditional Periodic Unit Cell based approach -- serves as a reduced geometrical model for materials with stochastic heterogeneous microstructure, enabling an efficient synthesis of microstructural realizations. To facilitate macroscopic analyses with a fully resolved microstructure generated with Wang tiles, we develop a reduced order modelling scheme utilizing pre-computed characteristic features of the tiles. In the offline phase, inspired by the computational homogenization, we extract continuous fluctuation fields from the compressed microstructural representation as responses to generalized loading represented by the first- and second-order macroscopic gradients. In the online phase, using the ansatz of the Generalized Finite Element Method, we combine these fields with a coarse finite element discretization to create microstructure-informed reduced modes specific for a given macroscopic problem. Considering a two-dimensional scalar elliptic problem, we demonstrate that our scheme delivers less than a 3% error in both the relative $L_2$ and energy norms with only 0.01% of the unknowns when compared to the fully resolved problem. Accuracy can be further improved by locally refining the macroscopic discretization and/or employing more pre-computed fluctuation fields. Finally, unlike the standard snapshot-based reduced-order approaches, our scheme handles significant changes in the macroscopic geometry or loading without the need for recalculating the offline phase, because the fluctuation fields are extracted without any prior knowledge on the macroscopic problem.