Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks

Norbert Kovács; I.B.C.M. Rocha; F.D. van der Meer; C. Furtado; P. Camanho

doi:10.23967/data-driven.2025.014

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Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks

Norbert Kovács I.B.C.M. Rocha F.D. van der Meer C. Furtado P. Camanho

Year: 2025

DOI: 10.23967/data-driven.2025.014

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Keywords:

Computer science Artificial neural network Composite number Uncertainty quantification Multiscale modeling Artificial intelligence Machine learning Algorithm Chemistry

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Material Properties and Failure Mechanisms

Physical Sciences → Materials Science → Materials Chemistry

Machine Learning in Materials Science

Physical Sciences → Materials Science → Materials Chemistry

X-ray Diffraction in Crystallography

Physical Sciences → Materials Science → Materials Chemistry

Uncertainty Quantification in Multiscale Modeling of Composite Materials Using Physically Recurrent Neural Networks

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