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Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks

  • Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification
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Abstract

The design and development of materials with increased damage resilience is often impeded by the difficulty in establishing the precise linkages, with quantified uncertainty, between the complex details of the internal structure of materials and their potential for damage initiation. We present herein a novel machine-learning-based approach for establishing reduced-order models (ROMs) that relate the microstructure of a material to its susceptibility to damage initiation. This is accomplished by combining the recently established materials knowledge system framework with toolsets such as feedforward neural networks and variational Bayesian inference. The overall approach is found to be versatile for training scalable and accurate ROMs with quantified prediction uncertainty for the propensity to damage initiation for a variety of microstructures. The approach is applicable to a large class of challenges encountered in multiscale materials design efforts.

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Acknowledgements

The authors gratefully acknowledge support from ONR N00014-18-1-2879.

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  1. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Aditya Venkatraman, David Montes de Oca Zapiain & Surya R. Kalidindi

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  1. Aditya Venkatraman
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  2. David Montes de Oca Zapiain
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Correspondence to Surya R. Kalidindi.

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Venkatraman, A., Montes de Oca Zapiain, D. & Kalidindi, S.R. Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks. JOM 72, 4359–4369 (2020). https://doi.org/10.1007/s11837-020-04387-y

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