Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures

Abstract

The response of a composite material is the result of a complex interplay between the prevailing mechanics and the heterogenous structure at disparate spatial and temporal scales. Understanding and capturing the multiscale phenomena is critical for materials modeling and can be pursued both by physical simulation-based modeling as well as data-driven machine learning-based modeling. In this work, we build machine learning-based data models as surrogate models for approximating the microscale elastic response as a function of the material microstructure (also called the elastic localization linkage). In building these surrogate models, we particularly focus on understanding the role of contexts, as a link to the higher scale information that most evidently influences and determines the microscale response. As a result of context modeling, we find that machine learning systems with context awareness not only outperform previous best results, but also extend the parallelism of model training so as to maximize the computational efficiency.

A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics

“…Thirdly, the performances of different regression models are compared, showing that a random forest regression model outperformed the considered support vector regression model and M5 model tree in terms of accuracy by only a moderately increased training time compared to the M5 model tree. This approach was extended by Liu et al (2017) through considering context detection, i.e., "finding the right high-level, low dimensional, knowledge representation in order to create coherent learning environments" (Liu et al, 2017). In this regard, a two-step approach is used.…”

“…Three strategies of identifying the microstructure macro similarities are investigated and their performance compared. These strategies include context detection based on volume fractions alone, on "designed macroscale microstructure descriptors" (Liu et al, 2017) and on pair correlation functions. The results showed an improvement of 38% compared to the best results presented by Liu et al (2015b).…”

Deep materials informatics: Applications of deep learning in materials science

“…The best performing CNN architecture for this problem consisted of six layers, with two convolution layers and two fullyconnected layers. The accuracy of the deep learning model was compared against the MKS method, [73] and two ML-based methods called single-agent method [75] and multi-agent method, [76] which is essentially a hierarchical application of the single-agent method. On contrast-10 dataset, the MKS method resulted in a mean absolute strain error (MASE) of 10.86%, while the singleagent and multi-agent methods gave a MASE of 13.02% and 8.04%, respectively.…”

Microstructural Materials Design Via Deep Adversarial Learning Methodology

“…To date, Computational Materials Design (CMD) has revolutionarily changed the way advanced materials are developed [1][2][3][4][5][6][7][8]. In the plethora of successes in CMD [9][10][11][12][13][14][15], microstructure sensitive design [16] has shown its significance in driving the rapid discovery and manufacturing of new materials. In designing material microstructures, the appropriate design representation of microstructures determines its ultimate success.…”