Data-Driven Multi-Scale Modeling and Optimization for Elastic Properties of Cubic Microstructures

Hasan, Md Mahmudul; Mao, Yuwei; Choudhary, Kamal; Tavazza, Francesca; Choudhary, Alok; Agrawal, Ankit; Acar, Pınar

doi:10.1007/s40192-022-00258-3

Cited by 7 publications

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inverse problems are one of the most important problems in science as they can help us understand the unknown causes leading to the observations. Thus, it is extensively used in various scientific fields, such as geophysics, health care and materials science [17][18][19][20][21][22][23][24]. Discovering microstructures that exhibit given properties is one of the inverse problems focused on structure-property linkage in materials, which is explored in this work.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

Mao

Yang

Jha

et al. 2022

Integr Mater Manuf Innov

View full text Add to dashboard Cite

There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure–property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

show abstract