Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks

Schwarzer, Max; Rogan, Bryce; Ruan, Yadong; Song, Zhengming; Lee, Diana Y.; Percus, Allon G.; Chau, Viet T.; Moore, Bryan A.; Rougier, Esteban; Viswanathan, Hari; Srinivasan, G.

doi:10.1016/j.commatsci.2019.02.046

Cited by 78 publications

(46 citation statements)

References 29 publications

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“…Pooling and normalization layers allow for stepwise data simplification and for variable feature sizes, respectively. While the suitability of CNNs for materials science may not be immediately apparent, there are examples of direct applications such as materials texture recognition (Cang and Ren, 2016;Lubbers et al, 2017;Cecen et al, 2018), as well as indirect application examples in which e.g., non-visual materials data may be interpreted (Schwarzer et al, 2019).…”

Section: Short Overview and Description Of Machine Learning And Data mentioning

confidence: 99%

“…Instead, they used random forests and decision trees to efficiently perform an uncertainty quantification. Schwarzer et al (2019) circumvented the challenge of needing a large experimental dataset that is statistically meaningful via using a significant number of simulations; thus, the accuracy could be increased. For that, a deep neural network was used; specifically, a graph convolutional network for fracture feature recognition within the material.…”

Section: Predictivementioning

confidence: 99%

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

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Machine learning tools represent key enablers for empowering material scientists and engineers to accelerate the development of novel materials, processes and techniques. One of the aims of using such approaches in the field of materials science is to achieve high-throughput identification and quantification of essential features along the process-structure-property-performance chain. In this contribution, machine learning and statistical learning approaches are reviewed in terms of their successful application to specific problems in the field of continuum materials mechanics. They are categorized with respect to their type of task designated to be either descriptive, predictive or prescriptive; thus to ultimately achieve identification, prediction or even optimization of essential characteristics. The respective choice of the most appropriate machine learning approach highly depends on the specific use-case, type of material, kind of data involved, spatial and temporal scales, formats, and desired knowledge gain as well as affordable computational costs. Different examples are reviewed involving case-by-case dependent application of different types of artificial neural networks and other data-driven approaches such as support vector machines, decision trees and random forests as well as Bayesian learning, and model order reduction procedures such as principal component analysis, among others. These techniques are applied to accelerate the identification of material parameters or salient features for materials characterization, to support rapid design and optimization of novel materials or manufacturing methods, to improve and correct complex measurement devices, or to better understand and predict fatigue behavior, among other examples. Besides experimentally obtained datasets, numerous studies draw required information from simulation-based data mining. Altogether, it is shown that experiment-and simulation-based data mining in combination with machine leaning tools provide exceptional opportunities to enable highly reliant Bock et al. Machine Learning in Materials Mechanics identification of fundamental interrelations within materials for characterization and optimization in a scale-bridging manner. Potentials of further utilizing applied machine learning in materials science and empowering significant acceleration of knowledge output are pointed out.

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Section: Short Overview and Description Of Machine Learning And Data mentioning

confidence: 99%

Section: Predictivementioning

confidence: 99%

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

et al. 2019

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“…While the long short-term memory network [14] considers the global semantic information of the text from the character information, it only expands in time; thus, it cannot effectively capture the deeper level abstract features. Each node in the character-level convolutional layer [15][16][17], when extracting the character features of the named-entity, transmits the feature information obtained by itself to the next adjacent node after nonlinear variation. This is then passed onto multiple nodes nearby to achieve the accumulation of character information.…”

Section: Feature Learningmentioning

confidence: 99%

Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network

et al. 2020

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Traditional methods for identifying naming ignore the correlation between named entities and lose hierarchical structural information between the named entities in a given text. Although traditional named-entity methods are effective for conventional datasets that have simple structures, they are not as effective for sports texts. This paper proposes a Chinese sports text named-entity recognition method based on a character graph convolutional neural network (Char GCN) with a self-attention mechanism model. In this method, each Chinese character in the sports text is regarded as a node. The edge between the nodes is constructed using a similar character position and the character feature of the named-entity in the sports text. The internal structural information of the entity is extracted using a character map convolutional neural network. The hierarchical semantic information of the sports text is captured by the self-attention model to enhance the relationship between the named entities and capture the relevance and dependency between the characters. The conditional random fields classification function can accurately identify the named entities in the Chinese sports text. The results conducted on four datasets demonstrate that the proposed method improves the F-Score values significantly to 92.51%, 91.91%, 93.98%, and 95.01%, respectively, in comparison to the traditional naming methods.

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“…In our previous works [61,62], we have utilized machine learning approaches to efficiently emulate the high-fidelity model. The key QoIs Moore et.…”

Section: Introductionmentioning

confidence: 99%

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials

et al. 2019

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In brittle fracture applications, failure paths, regions where the failure occurs and damage statistics, are some of the key quantities of interest (QoI). High-fidelity models for brittle failure that accurately predict these QoI exist but are highly computationally intensive, making them infeasible to incorporate in upscaling and uncertainty quantification frameworks. The goal of this paper is to provide a fast heuristic to reasonably estimate quantities such as failure path and damage in the process of brittle failure. Towards this goal, we first present a method to predict failure paths under tensile loading conditions and low-strain rates. The method uses a k-nearest neighbors algorithm built on fracture process zone theory, and identifies the set of all possible pre-existing cracks that are likely to join early to form a large crack. The method then identifies zone of failure and failure paths using weighted graphs algorithms. We compare these failure paths to those computed with a high-fidelity fracture mechanics model called the Hybrid Optimization Software Simulation Suite (HOSS). A probabilistic evolution model for average damage in a system is also developed that is trained using 150 HOSS simulations and tested on 40 simulations. A non-parametric approach based on confidence intervals is used to determine the damage evolution over time along the dominant failure path. For upscaling, damage is the key QoI needed as an input by the continuum models. This needs to be informed accurately by the surrogate models for calculating effective moduli at continuum-scale. We show that for the proposed average damage evolution model, the prediction accuracy on the test data is more than 90%. In terms of the computational time, the proposed models are ≈ O ( 10 6 ) times faster compared to high-fidelity fracture simulations by HOSS. These aspects make the proposed surrogate model attractive for upscaling damage from micro-scale models to continuum models. We would like to emphasize that the surrogate models are not a replacement of physical understanding of fracture propagation. The proposed method in this paper is limited to tensile loading conditions at low-strain rates. This loading condition corresponds to a dominant fracture perpendicular to tensile direction. The proposed method is not applicable for in-plane shear, out-of-plane shear, and higher strain rate loading conditions.

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Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks

Cited by 78 publications

References 29 publications

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

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

Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network

Surrogate Models for Estimating Failure in Brittle and Quasi-Brittle Materials

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