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

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 here...

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Vydané v:JOM (1989) Ročník 72; číslo 12; s. 4359 - 4369
Hlavní autori: Venkatraman, Aditya, Montes de Oca Zapiain, David, Kalidindi, Surya R.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.12.2020
Springer Nature B.V
Predmet:
Artificial neural networks
Augmenting Physics-based Models in ICME with Machine Learning and Uncertainty Quantification
Bayesian analysis
Chemistry/Food Science
Crack initiation
Damage
Datasets
Design optimization
Earth Sciences
Engineering
Environment
Fracture mechanics
Machine learning
Microstructure
Neural networks
Open source software
Physics
Principal components analysis
Reduced order models
Statistical inference
Uncertainty
ISSN:1047-4838, 1543-1851
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Shrnutí: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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ISSN:1047-4838
1543-1851
DOI:10.1007/s11837-020-04387-y