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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Veröffentlicht in:	JOM (1989) Jg. 72; H. 12; S. 4359 - 4369
Hauptverfasser:	Venkatraman, Aditya, Montes de Oca Zapiain, David, Kalidindi, Surya R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.12.2020 Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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