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
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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.
AbstractList	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.
Author	Venkatraman, Aditya Kalidindi, Surya R. Montes de Oca Zapiain, David
Author_xml	– sequence: 1 givenname: Aditya surname: Venkatraman fullname: Venkatraman, Aditya organization: George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology – sequence: 2 givenname: David surname: Montes de Oca Zapiain fullname: Montes de Oca Zapiain, David organization: George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology – sequence: 3 givenname: Surya R. surname: Kalidindi fullname: Kalidindi, Surya R. email: surya.kalidindi@me.gatech.edu organization: George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology
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CitedBy_id	crossref_primary_10_1016_j_msea_2021_142472 crossref_primary_10_1007_s40192_022_00282_3 crossref_primary_10_1103_8zzt_4b7z crossref_primary_10_1016_j_actamat_2024_120537 crossref_primary_10_1016_j_ijplas_2025_104319 crossref_primary_10_1016_j_mechmat_2023_104679 crossref_primary_10_1016_j_commatsci_2023_112074
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SubjectTerms	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
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Title	Reduced-Order Models for Ranking Damage Initiation in Dual-Phase Composites Using Bayesian Neural Networks
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