Distributional Inverse Homogenization

TL;DR

This paper introduces a noninvasive method called distributional inverse homogenization to infer microstructural statistics from bulk mechanical properties, combining homogenization theory with probabilistic modeling.

Contribution

It develops a novel inverse approach that leverages large datasets of macroscopic properties to learn microstructure variability, supported by theoretical and empirical validation.

Findings

Successfully demonstrated in 2D Voronoi microstructures

Theoretical foundation provided for 1D case

Surrogate model accelerates computations

Abstract

For many materials, macroscopic mechanical behavior is determined by an intricate microstructure. Understanding the relation between these two scales helps scientists and engineers design better materials. The relation which maps microstructure to bulk mechanical properties can be understood via the well-established theory of homogenization. However inverting the homogenization process, to recover microstructural information from measured macroscopic properties, is fraught with difficulties because of the averaging processes that underlie homogenization. Therefore, scientists and engineers usually need recourse to more invasive, often highly localized, investigations to learn about a microstructure. In this work, we develop a noninvasive methodology by which one can leverage large collections of measured bulk mechanical properties to learn information about the statistics of microstructure at a global level. We call this, distributional inverse homogenization. We study this problem in one and two dimensions, considering both periodic and stochastic homogenization. We demonstrate the methodology in the context of 2D Voronoi constructions and underpin the observed empirical success with theory in 1D. We also show how the natural spatial variability of microstructure can be exploited to gather data that enables distributional inversion. And we concurrently learn a surrogate model, approximating the homogenization map, that accelerates the resulting computations in this setting. The work formulates a new class of inverse problems, bridging ideas from probability and homogenization to facilitate the learning of microstructural material variability from macroscopic measurements.